research on plant pathology

Journal of Plant Pathology

Journal of Plant Pathology focuses on the fundamental and applied aspects of plant pathology.

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Volume 106, Issue 1

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Complete genome resource of cedecea neteri a2, the causal agent of pleurotus pulmonarius yellow rot disease in guangxi, china.

• Shengjin Wu
• Tomislav Cernava

The Journal of Plant Pathology Editors’ Choice May 2024

First report of a ‘ candidatus  phytoplasma trifolii’-related strain (16srvi-a) associated with watermelon in türkiye.

• Osman Çiftçi
• Deniz Çaplık

First report of Candidatus Phytoplasma trifolii (16SrVI-D) associated with little leaf disease of Nyctanthes arbor-tristis in the world

• Dibya Sree Dutta
• Manoj Kumar Kalita
• Palash Deb Nath

First report of Citrus concave gum-associated virus (CCGaV) on apple ( Malus spp.) in South Africa

• Gerhard Pietersen
• Seamus Morgan

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The Young Editorial Board below includes young researchers in training on the publishing process. They assist Editors with reviewing and evaluating manuscripts, in addition to standard reviewers.

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A plant expert on how he is reaching the public and policy makers with his research

Springer Nature's Sustainable Development Goals (SDG) Programme aims to connect the researchers who are tackling the world’s toughest challenges with the practitioners in policy and business who desperately need those insights to achieve their goals in improving the world, by making our publishing activities more visible to our key communities through a variety of channels. As part of the  SDG15 hub  launch, dedicated to life on land, we asked several experts how they work to make sure their related research is reaching those that need it most to make urgent decisions when it comes to the future of our planet.

Click here to read the contribution of Matteo Garbelotto, the Editor in Chief of the Journal of Plant Pathology, on how he is reaching the public and policy makers with his research.

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Phytopathology Research

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Botanical extracts control the fungal pathogen Colletotrichum boninense in smallholder production of common bean

Authors: Tamia M. Kushaha, Angela G. Mkindi, Ernest R. Mbega, Philip C. Stevenson and Steven R. Belmain

Four sugarcane ScDIR genes contribute to lignin biosynthesis and disease resistance to  Sporisorium scitamineum

Authors: Xiufang Li, Zongling Liu, Haoming Wu, Zhuoxin Yu, Jiaorong Meng, Haiyun Zhao, Xingli Deng, Yizu Su, Baoshan Chen and Ru Li

The conserved GOLD domain in the Plasmodiophora brassicae effector Pb257 is required for triggering cell death and root swelling

Authors: Hui Yang, Rong Liao, Chuang Jin, Xiaoya Feng, Yang Zhang, Xinyue Wang, Yuge Zhang, Liang Yu and Wenming Wang

Population genetic analyses highlight an eastward dispersal of Puccinia striiformis f. sp. tritici from the Shaanxi province to the North China Plain

Authors: Congying Zhou, Xuan Lv, Xinlei Qi, Ahsan Abdullah, Qiuyu Sun, Mingliang Ding, Yingwen Bai, Ziqian Yang, Zhifang Wang and Zhanhong Ma

Joint application of plant immunity-inducing elicitors and fungicides to control Phytophthora diseases

Authors: Rubin Chen, Dicheng Ma, Yazhou Bao, Weijie Wang, Dandan Du, Xujun Chen, Daolong Dou and Xiangxiu Liang

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From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

Authors: Clive H. Bock, Jayme G. A. Barbedo, Emerson M. Del Ponte, David Bohnenkamp and Anne-Katrin Mahlein

WRKY transcription factors: evolution, binding, and action

Authors: Xujun Chen, Cheng Li, Han Wang and Zejian Guo

NAC transcription factors in plant immunity

Authors: Xi Yuan, Hui Wang, Jiating Cai, Dayong Li and Fengming Song

Suitable methods for isolation, culture, storage and identification of wheat blast fungus Magnaporthe oryzae Triticum pathotype

Authors: Dipali Rani Gupta, Musrat Zahan Surovy, Nur Uddin Mahmud, Moutoshi Chakraborty, Sanjoy Kumar Paul, Md. Shaid Hossain, Pallab Bhattacharjee, Md. Shabab Mehebub, Kanistha Rani, Rumana Yeasmin, Mahfuzur Rahman and Md Tofazzal Islam

The Correction to this article has been published in Phytopathology Research 2020 2 :32

Wheat blast: a new threat to food security

Authors: M. Tofazzal Islam, Dipali Rani Gupta, Akbar Hossain, Krishna K. Roy, Xinyao He, Muhammad R. Kabir, Pawan K. Singh, Md. Arifur Rahman Khan, Mahfuzur Rahman and Guo-Liang Wang

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About the Editor

Thematic Series

Plant Pathology Research in China: A Centennial View Edited by: Prof. Jun Liu and Prof. Xiaorong Tao

Enjoy reading this collection and free downloads of articles! The initial call for papers can be found here .

Good News: Phytopathology Research received the IF 2022 as 3.4!

We are pleased to announce that Phytopathology Research  received the new Impact Factor ( IF 2022 ) as 3.4 , which placed the journal as #69/238 ( Q2 ) in the category of Plant Sciences!

We would like to take this opportunity to thank all the authors, reviewers, readers and editorial board members for their continuous support to the journal, and making this exciting development possible!

Call for Reading

Enjoy reading the  10 highly cited articles that  Phytopathology Research published in 2022-2023. Reverse transcription-recombinase-aided amplification and CRISPR/Cas12a-based visual detection of maize chlorotic mottle virus

Ubiquitination in the rice blast fungus Magnaporthe oryzae : from development and pathogenicity to stress responses

Fighting wheat rusts in China: a look back and into the future

The histone deacetylase HOS2 controls pathogenicity through regulation of melanin biosynthesis and appressorium formation in Colletotrichum gloeosporioides

Combatting Fusarium head blight: advances in molecular interactions between Fusarium graminearum and wheat

Global distribution and management of peach diseases

Advances in understanding the soil-borne viruses of wheat: from the laboratory bench to strategies for disease control in the field

Deciphering the genome of Simplicillium aogashimaense to understand its mechanisms against the wheat powdery mildew fungus Blumeria graminis f. sp. Tritici

Biosynthesized silver nanoparticles inhibit Pseudomonas syringae pv. tabaci by directly destroying bacteria and inducing plant resistance in Nicotiana benthamiana

Biological and molecular characterizations of fluxapyroxad-resistant isolates of Botrytis cinerea

Aims and scope

Phytopathology Research is an open access journal dedicated to advancing our understanding of plant diseases and developing effective environment-friendly measures for disease control. The journal publishes fundamental and applied research on broad aspects of plant diseases. These include but are not limited to genetics and molecular biology of plant disease resistance or susceptibility, molecular analysis of relevant traits in agriculturally important phytopathogens, the ecology of pathogens and plant-associated beneficial micro-organisms, disease etiology, epidemiology and disease management, and technical innovations that advance the phytopathology research. Articles are selected based on novelty, importance, scientific validity, and interest to the readers.

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Phytopathology Research is the official journal of the Chinese Society for Plant Pathology

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2022 Citation Impact 3.4 - 2-year Impact Factor 4.2 - 5-year Impact Factor 1.282 - SNIP (Source Normalized Impact per Paper) 0.841 - SJR (SCImago Journal Rank)

2023 Speed 7 days submission to first editorial decision for all manuscripts (Median) 125 days submission to accept (Median)

2023 Usage  260,244 downloads 184 Altmetric mentions 

ISSN: 2524-4167

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Digital plant pathology: a foundation and guide to modern agriculture

Matheus thomas kuska.

1 North Rhine-Westphalia Chamber of Agriculture, Gartenstraße 11, 50765 Cologne, Germany

René H. J. Heim

2 Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany

Ina Geedicke

Kaitlin m. gold.

3 Plant Pathology and Plant-Microbe Biology College of Agriculture and Life Science, Cornell University, Cornell AgriTech, 15 Castle Creek Drive, Geneva, 14456 USA

Anna Brugger

4 Bildungs- und Beratungszentrum Arenenberg, Arenenberg 8, 8268 Salenstein, Switzerland

Stefan Paulus

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host–pathogen–environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

Introduction

The changing attitude of society towards a more sustainable planet, which is nowadays termed as ‘neo-ecology’, is changing our common agriculture in a drastic way. Stockbreeding, crop cultivation and plant protection are critically re-examined in the view of environmental and human protection strategies to meet the standards of the ‘agriculture green development’ (Davies and Shen 2020 ). Currently, agricultural land covers approximately five billion hectares, which is 38% of the available land on our planet (annual data FAO 2018 ). Agriculture must be updated in some aspects to meet rigorous environmental protection targets. However, a sustainable increase in productivity is inevitable because human population is growing continuously. Due to the COVID-19 pandemic, the proportion of undernourished people even increased from 650 (~ 8.4%) up to 811 million (~ 9.9%) (annual data FAO 2021 ).

To guarantee sufficient food production, unnecessary production loss in agriculture must be avoided. Globally, integrated pest management (IPM) has reduced harvest losses of the five major food crops (i.e., wheat, rice, maize, potato, soybean) to 20–40% which are attributed to plant pathogens and pests (Savary et al. 2019 ). Unfortunately, most fields are too large for growers to cost-effectively monitor yield-reducing causes, such as diseases, at regular time intervals. In addition, the detection and exact determination are complex. The field of remote sensing offers methods for high temporal- and spatial-resolution monitoring, that can be used to efficiently deploy ground analysis and remediation action to diseased plants before financial losses incur and disease epidemics emerge. The application of remote sensing methods in plant pathology detection is based on the fact that plant pathogens and pests change the way light interacts with leaves and canopies.

Remote sensing, at its core, is the use of non-contact, often optical sensors such as RGB, multi- and hyperspectral, thermal, chlorophyll fluorescence, and 3D-imaging, to obtain information about processes occurring in the natural and artificial landscape. Optical sensors offer the opportunity for non-destructive disease monitoring at different scales (Mahlein 2016 ). Next to common techniques for plant disease/pest monitoring, which range from molecular assays to smartphone applications, sensors optimize and reduce the human effort of disease detection in the field (Silva et al. 2021 ). Though, seemingly straightforward, disease detection, using remote sensing methods in the field, can be complex. Plant diseases themselves are complex as well. They often exhibit a heterogeneous distribution within crop stands and are highly dynamic in time and space due to dynamic interactions between living organisms within an ever-changing environment. Some of the most current challenges, research topics and achievements of digital plant pathology are summarized in Fig.  1 , from a phytopathology perspective. It should highlight, that the main goal of digital plant pathology must be to manage farmer’s needs.

Achievements, challenges, and current research of digital plant pathology for adaption into the field practice. Challenges are to capture and explain the complexity resulting from the triangular relationship of sensor, pathogen, and environment. Implementing new methods is hindered by the lack of plant protection and the growing resistances. The analysis of big data is labor-intensive and needs sophisticated data-driven approaches, which can only be sufficiently interpreted by a multidisciplinary team. Currently, the development of agricultural robots, which can detect, assess and operate autonomously, is a research focus and, in the view of weeding, are very promising. Personal consulting is a driving force to introduce new technologies and digital possibilities into agriculture. Thereby, computer/software approaches, as well as smart solutions enable fast and interconnected access to global data

Therefore, we are aiming at providing a potential new direction for digital plant pathology research. We are taking a look at some milestones of digital plant pathology and explore the state-of-the-art imaging techniques and analysis methods. With this insight, we are creating a snapshot of the current technical state of applied digital plant pathology and ask the question if we already reached the goal of optimizing manual disease detection.

Digital plant pathology

Almost a century ago,  in 1927, Neblette showed that aerial photography (RGB) enables disease survey in agricultural crops. In 1933, Bawden discovered in the lab that a black-and-white representation of an infrared photography resulted in high contrasts between necrotic leaf spots caused by potato viruses. The infrared images were compared to panchromatic (i.e., black-and-white images sensitive to all wavelengths of visible light) images and no obvious contrast was visible. When the same was done with tobacco leaves, the opposite happened, and panchromatic images showed the greatest contrast compared to infrared filter images. The differences were explained by the different makeup of the necrotic areas. Necrotic cells in potato contained chemical break-down products while necrotic cells in tobacco were merely dead empty cells that differed in color compared to the rest of the leaf cells. These findings set the stage for the use of different spectral bands to detect differences in plant health.

Technical development of optical sensors increased and Colwell ( 1956 ) remotely determined wheat rust and other diseases of grains by using military helicopters and infrared-filter cameras, as well as a spectrometer at oblique and nadir observation angles. Colwell suggested to test different combinations of spectral bands for disease detection. Based on the literature and his investigated photos and spectral reflectance curves, he proposed a new view on the interaction of light with plants and the assessment and interpretation of crop photos for plant diseases. Colwell contributed an important theoretical framework that is still of utmost importance in digital plant pathology.

Since 2000, the idea of “foliar functional traits” has strongly emerged as a unifying concept in terrestrial remote sensing to better understand both natural variabilities in vegetation function and variability in response to stress (DuBois et al. 2018 ). Many traits shown to strongly correlate with natural and stress-induced variation in plant function (Wright et al. 2004 ) can be quantified and mapped with imaging spectroscopy (Townsend et al. 2003 ; Ustin et al. 2004 ; Asner and Martin 2009 ; Ustin and Gamon 2010 ; Heim et al. 2015 ; Wang et al. 2020 ). Originating in terrestrial ecology, the use of spectroscopy combined with chemistry and taxonomy has been coined as “spectranomics” (Asner and Martin 2009 , 2016 ; Zhang et al. 2020 ). The foundational components of this approach are: (i) plants have chemical and structural fingerprints that become increasingly unique when additional constituents are incorporated (Ustin et al. 2004 ) and (ii) spectroscopic signatures determine a portfolio of chemicals found in plants (Jacquemoud et al. 1995 ).

When applied to plant disease, spectranomics allows for accurate and non-destructive detection of direct and indirect changes to plant physiology, morphology, and biochemistry which induces the disease, both pre- and post-symptomatically (Arens et al. 2016 ; Couture et al. 2018 ; Fallon et al. 2020 ; Gold et al. 2020 ). Beneficial (Sousa et al. 2021 ) and parasitic (Zarco-Tejada et al. 2018 ) plant–microbe interactions impact a variety of plant traits that can be remotely sensed. Changes to narrowband wavelengths, have proven valuable for plant disease sensing due to their sensitivity to a range of foliar properties (Curran 1989 ). The ultraviolet range (UV; 100–380 nm) is influenced by secondary plant metabolites, while the visible range (VIS; 400–700 nm) is influenced by primary metabolites such as pigments. Internal scattering processes and the structure of a leaf alter the near-infrared range (NIR; 700–1000 nm) while chemicals and water show alterations within the short-wave infrared (SWIR; 1000–2500 nm) (Carter and Knapp 2001 ). This means, that the nutrient content (Gillon et al. 1999 ; Zhai et al. 2013 ; Singh et al. 2015 ; Wang et al. 2016 , 2020 ), water status (Gao 1996 ), photosynthetic capacity (Oren et al. 1986 ), physiology (Serbin et al. 2019 ), phenolics (Kokaly and Skidmore 2015 ), secondary metabolites (Couture et al. 2013 , 2016 ) and leaf and cell structure (Mahlein et al. 2012 ; Leucker et al. 2016 ; Kuska et al. 2015 , 2017 ), which are changed by diseases are displayed in changes of the spectral reflectance. The foundational spectranomics approach offers an explanation as to why sensing technologies are capable of disease detection in the first place. Remote imaging spectroscopy assesses the sum impact of the fundamental biochemical, structural and physiological processes that underlie the diseased plant phenotype (Mahlein et al. 2012 , Leucker et al. 2016 , Kuska et al. 2017 , 2018a , 2018b , 2019 , Zarco-Tejada et al. 2018 , 2021 ; Asner et al. 2018; Sapes et al. 2021 ). Further ranges of the electromagnetic spectrum can also provide interesting information, but often it is not possible to characterize the determined changes to a specific cause (Mahlein 2016 ; Simko et al. 2016 ). As an example, infrared (8–12 µm) light can be determined with thermal cameras, which return a “calibrated” temperature of the plant. The temperature of plants correlates very strongly with the transpiration rate. In addition to recording the water balance of the plant or the crop, this enables the detection of potential drought stress before it becomes visible. Although the sensitivity of thermography and chlorophyll fluorescence sensors is very high, both techniques lack of the possibility to differentiate between abiotic or biotic stress and with it of a causal connection to a specific disease (Mahlein 2016 ; Simko et al. 2016 ). However, a combination of sensors can indeed enable a specific characterization of plant diseases.

Within the last couple of years, Zarco-Tejada et al. ( 2018 ) were able to use a combination radiative transfer and machine learning approach (Hernández-Clemente et al. 2019 ) to pre-symptomatically detect Xylella fastidiosa infection in olive trees. This was achieved through a combination of hyperspectral NIR, thermal, and solar-induced fluorescence measurements. The authors found that spectral-plant trait alterations in response to X. fastidiosa infection in both spectral stress indicators and pigment degradation traits, particularly the chlorophyll degradation phaeophytinization-based spectral trait (NPQI), were essential for distinguishing asymptomatically infected plants from both symptomatic and healthy plants. Following up on this work, the authors found that NPQI was only indicative of asymptomatic X. fastidiosa infection in irrigated almond groves. This eventually led to the discovery of the existence of divergent pathogen- and host-specific spectral pathways in response to abiotic and biotic stresses that yield a similar visual manifestation (Zarco-Tejada et al. 2021 ). Even though both drought and bacterial infection cause the plant to wilt, the mechanisms by which they do so are different, and this difference could be captured with spectroscopy. The authors then used the thermal crop water stress index (CWSI) to uncouple the confounding interaction to improve their misclassification accuracy from 37% and 17% to 6.6% and 6.5%, respectively. By assessing spectral trait measurements that captured the underlying physiochemical origin of their diseased plant phenotype, the authors were able to develop a robust disease detection and differentiation methodology for mapping asymptomatic X. fastidiosa infection in multiple crops at scale. This success bolsters and lends hope to ongoing investigations that seek to detect diseases in real-world, multi-stress environments (Fig.  1 ).

Disease management in the field: can spectral imaging provide the required digital information to control plant diseases and pests?

For disease management, weather-based consultation and forecasting systems (e.g., proPlant Expert.com; RANTISMA), enable the best plant protection measures by their warning services of appearing pests and diseases, since the early 1990s (Newe et al. 2003 ). The manual field check by the farmer is still necessary, but with digital consulting systems, time management and the process for a successful plant protection measures is optimized (summarized in Damos 2015 ). However, many techniques and methods are still labor-intensive, and therefore, further progress is necessary. Nilsson ( 1995 ) already concluded in his review that remote sensing offers a wider range of sensors and application scales ranging from satellites to ground-based platforms. Nevertheless, depending on the scale, pre-symptomatic and disease-specific detection, as well as the influence of the environment remained a major challenge (Mahlein et al. 2012 ). This is based on the fact, that plant–microbe interactions are subtle changes in biochemistry and structure. The interactions can be described in compatible (plant pathogenesis) and incompatible (plant resistance response) interactions. To differentiate pathogen attack symptoms, resistance reactions, abiotic stress and spectral signatures of healthy leaves, each of these states had to be characterized in detail (Carter and Knapp 2001 ). Multi- and hyperspectral imaging is the preferable technique to study such interactions from the cell level to the canopy (Bohnenkamp et al. 2019a , b , 2021 ).

Variances within and between spectral reflectance signatures were already remotely determined with Landsat-2 imagery. It was used to monitor an epidemic in Pakistan in the late 1970s, the first -ever use of space-borne sensing to monitor disease (Nagarajan et al. 1984 ). However, a better spatial resolution was needed to precisely explore infections in the field, especially to characterize a pathogen. As the equipment to provide higher resolution was still in an early development stage , spatial resolution, spectral resolution, and costs were closely related. For instance, the amount of generated film could not be stored at reasonable costs, was tedious to analyze as human raters had to screen the images, and no computers were available to perform pixel-wise calculations. The overall progress of remote sensing for abiotic and biotic plant stress was summarized by Jackson ( 1986 ).

Two decades later, Chaerle et al. ( 2007 ) analyzed resistant tobacco plants, and those susceptible to the tobacco mosaic virus; also, they looked at Cercospora beticola on sugar beet using thermal imaging and chlorophyll fluorescence. They enabled a pre-symptomatic detection and indicated that their studied plant-pathogen interactions could be distinguished. Next, studies using hyperspectral imaging showed that it was possible to discriminate and characterize symptoms of sugar beet diseases, such as Heterodera schachtii , Rhizoctonia solani , Cercospora beticola , Uromyces betae and Erysiphe betae (Hillnhütter and Mahlein 2008 ; Mahlein et al. 2010 ; Hillnhütter et al. 2012 ). Hyperspectral imaging (HSI) further enabled the research community to get a deeper understanding of plant-pathogen interactions. Leucker et al. ( 2016 ) were able to display different disease severities of sugar beet leaves inoculated with C. beticola caused by quantitative trait loci (QTL). The increase in phenolic compounds and structural discontinuities caused by tissue collapse, in response to fungal toxins, explain the substantial decrease in reflectance of QTL leaves. Using HSI in the SWIR-range, a variety of micro- and macronutrients such as nitrogen, magnesium, sodium, iron, or copper could also be identified in corn and soybeans undergoing water stress (Pandey et al. 2017 ). In addition, HSI can be extended to all parts of the plant. For example, Alisaac et al. ( 2019 ) showed that HSI of wheat spikelets infected by Fusarium head blight allows for the identification of mycotoxins which was confirmed by quantification of fungal DNA.

Importantly, HSI comes with numerous advantages compared to classical visual monitoring or other analytical methods. It can be applied at different scales—from the cellular level for investigating plant tissue in combination with microscopes, over the individual plant scale in greenhouses or climate chambers, to the canopy scale in field applications with cameras mounted on unmanned aerial vehicles or airplanes (Bohnenkamp et al. 2019a , b , 2021 ; Heim et al. 2019a ). However, in all cases, the analysis of HSI data must be done with care as a great complexity results from a triangular relationship between sensor, pathogen, and environment (Fig.  1 ). This relationship is further complicated by large amounts of often co-linear data (Thomas et al. 2018a ). The effective analysis and interpretation of hyperspectral data are limiting factors for an implementation into plant phenotyping or precision agriculture Mahlein et al. ( 2018 ). Automated analysis pipelines and sophisticated data mining and machine learning approaches are necessary to “uncover the spectral language of plants”, as it was shown by Wahabzada et al., ( 2016 ).

Data handling and machine learning

Once imaging has been completed, a data analysis pipelines must be developed and implemented to ensure retrieval of meaningful information. In Fig.  2 , we are presenting a workflow diagram that proposes a potential new multidisciplinary workflow for digital plant pathology research. Several requirements are prerequisites and different subsequent or parallel steps are necessary. After successfully measuring plant data, preprocessing needs to be performed. Steps like de-noising, smoothing, calibration, image segmentation, and outlier removing must be added to transfer the image data to features that can be used as input for machine learning routines (Paulus and Mahlein 2020 , Behmann et al. 2015 ). Literature shows not only the importance of this step but also the huge effort that is required for different sensors in greenhouses and in the field (Bohnenkamp et al. 2021 , 2019a , b ; Thomas et al. 2018b ). Hyperspectral measurements either use the raw reflectance signal as input or, for reduction of data complexity, vegetation indices like the NDVI or OSAVI (Bohnenkamp et al. 2019a , b ; Rouse et al. 1974 , Rondeaux 1996 ). Even though data complexity is reduced, vegetation indices retain their predictive power and can be used for phenotyping approaches with comparably low data input and subtle features. An example was shown for light leaf spot on oilseed rape plants (Veys et al. 2019 ). Multispectral approaches in the field can be used in similar ways but usually require a much higher effort for registration and data calibration due to the large area of interest and the fact that the environmental conditions are changing during capturing (Tmušić et al. 2020 ). For such circumstances, it is discussed how 3D imaging can provide necessary information for data calibration (Paulus 2020 ; Paulus et al. 2014 ).

The workflow for the interpretation of sensor data using machine learning and linking it to biological processes, using supervised learning and feature importance methods, is shown. Adding the biological knowledge to the interpretation of features would allow for a more mechanistic and transparent machine learning approach as is currently the case. Each step in the process is often performed by a single expert. Thus, detailed knowledge of methods—especially in machine learning—is often not available. An approach involving experts from multiple disciplines would improve current workflows

Machine learning provides approaches to give meaning to the data. Supervised learning is used to train a classifier to separate different classes of infection or diseases (Rumpf et al. 2010 ). Therefore, a labeled dataset to train the model is essential. Commonly, the labeled dataset is split into three different subsets including a training set, a validation set, and a test set. The training set is used to generate a model, the validation set is used to validate it and to perform a fine tuning, and the final test set is then used to calculate various accuracy and error metrics. Comparable to conventional data analysis methods where rules are postulated to analyze the data, machine learning enables to learn these rules by the above-mentioned training process. Although data labelling might require intense manual work, these methods enjoy great popularity in plant science.

A type of machine learning algorithm, the neural network, has been rediscovered during the last decade. Invented during the early 1940s (McCulloch and Pitts 1943 ), this machine learning approach became only popular later with the development of high computational power. Neural networks use the captured images for segmentation, classification, or regression tasks (Barbedo 2021 ). It is to consider that the data, which is used to train these algorithms, must be of high quality and in a proper quantity to realize results with a high accuracy and low aberration.

A deeper insight into the importance of the input variables is also enabled by further supervised machine learning. Adapted algorithms like Boruta or Recursive Feature Elimination (Chen et al. 2020 ) provide an importance rating for the machine learning features. When used on hyperspectral plant disease data, these techniques can reveal spectral regions of important wavelength for identifying infected plants (Brugger et al. 2021 ). In contrast to supervised machine learning methods, unsupervised methods do not need any labeled data or data splitting. These clustering approaches like k-means or hierarchical clustering combine data of similar features and thus give semantic to the data by finding patterns of similarity (Wahabzada et al. 2015 ). However, results are hard to interpret and need then labelled data to be evaluated. Yet, these routines can be used to find groups of similarities, which have not been noticed before.

At this point, biological insight is needed to connect the output of the machine learning methods to plant- and infection processes (Fig.  2 ). Recently, approaches integrated expert knowledge as active learning processes in the analysis pipeline, this resulted in significantly improved quality and interpretability of machine learning outputs (Schramowski et al. 2020 ). To exploit such sophisticated data-driven approaches for real applications by agricultural experts, the models must be biological interpretable, which are now known as “white-box machine learning algorithms” (Fig.  1 ). They earned this name by aiming at being more explainable and transparent for users interested in the underlying cause for algorithmic outputs. The opposite would be the previous type of algorithms known as “black-box algorithms”. Latest developments show publicly available software libraries, such as the caret package (Kuhn 2008 ), Keras or Tensorflow (Géron 2019 ). Nevertheless, these models are only powerful through the underlying training data and rely on high-quality annotated data.

Digitalization in agricultural practice: are robots the better farmer?

Since the last turn of the millenium, researchers gained confidence in deploying unmanned terrestrial and aerial vehicles (Fig.  1 ). These could be equipped with reflectance-based sensors for disease detection with enhanced spatial resolutions allowing for better discrimination between biotic and abiotic stress. Some systems reached a work rate of 3 ha/h (West 2003 ). Still, variations in illumination intensity, sun/sensor orientation, and/or background soil reflection were impairing consistent and high-quality data retrieval. Another problem turned out to be soil dust leading to detection errors and physical damage to the crops through the vehicle itself. Nowadays, automatization, mechatronics, sensors, electrical engineering and artificial intelligence have reached a level that enables a high degree of autonomy for mobile platforms such as drones, cars, and robots (see Fig.  1 “achievements”). In agriculture, autonomous robots, equipped with sophisticated sensor systems, are the next digitalization step for precise fertilization, pesticide spot-spraying and automated mechanical weeding. Automated robotic applications might even offer an alternative for overcoming shortages of human workers, especially for labor-intensive tasks such as harvesting vegetables or manual weeding (Lowenberg-Deboer et al. 2020 ).

Furthermore, the implementation of automated systems re-designed agricultural production by considering spatial heterogeneities of plant pest distribution or input parameters such as nutrients, water, and agrochemicals (Saiz-Rubio and Rovira-Más 2020 ; Wegener et al. 2019 ). The development of robotic applications for crop management differs with respect to the crop type and cultivation system. One example is the usage of UAVs in the field which are releasing Trichogramma brassicae , a natural enemy against Ostrinia nubilalis (European corn borer), as a biological control in corn plants (Zhan et al. 2021 ). In contrast to the manual application of “Trichogramma bags”, UAVs enable a fast and practical application in open land.

In the greenhouse, higher levels of automation, such as robotic harvesting of e.g., pepper (Arad et al. 2020 ) or robotic plant protection measures in tomatoes (Rincón et al. 2020 ; Cantelli et al. 2019 ), are already implemented. Field crops bring a variation of challenges as they can be randomly distributed (e.g., cereals) or planted in rows (e.g., corn, sugar beet, cauliflower). A more and more frequently used application is the selective removal of weed within and between crop rows (Bakker et al. 2010 ) using actuators such as mechanical weeding tools, laser, stampers or milling heads.

Prototypes of these weeding robots have raised public awareness during the COVID-19 pandemic when trained workers for manual weeding were not available (Mitaritonna and Ragot 2020 ). A fast development can be seen for weeding robots, in particular for row crops. These robots are commercially available and can be equipped to deal with different working concepts. The first concept depends on a highly accurate GPS positioning of the seed pill (Griepentrog et al. 2006 ). Precise seeding with just a minimal error is the prerequisite for orientation and an automated weeding. The robots use the weeding tools on the complete field, except for the area around the planted seed. The second concept is independent of the seeding step. Using digital cameras and an adapted vision recognition system mostly depending on neural networks and a huge underlying training dataset, the robot is able to detect the crop rows and to adapt its position, heading and navigation path. Furthermore, the weeding tools can be positioned in between or across rows (Machleb et al. 2020 ).

Position-based systems need a highly precise GPS signal mostly coupled to a real-time-kinematic approach (RTK) which needs to be booked at local suppliers. Without this system, a proper operation is not possible. The system is based on pre-learned positions of seeds and is not aware of changes within the plant population. It does not detect if seeds do not germinate, were eaten by animals or rolled to another position during the sewing process. The robot will continue weeding around these spots or uses its tools where it assumes weeds regardless of the actual presence of the seed or seedling. Nevertheless, position-based systems are robust and operate independent of pre-learned image datasets. They need the data of the sowing process, which is commonly performed by specialist machines, which can do this step at high speed for many rows at the same time.

Sensor-based systems can operate on different types of fields independent of the GPS position and the field structure. Camera images were analyzed in an adapted image processing pipeline. Here, the systems need to separate between vegetation and soil and in a second step between crop and weed. Therefore, a machine learning model based on a neural net approach is used which needs to be trained beforehand on datasets with the same crop under various environmental conditions (Bawden et al. 2017 ). The bigger this training dataset, the better the segmentation of vegetation and soil, or crop and weed, respectively (Baretto et al. 2021 ). By extending this dataset, the robot can be adapted for usage on different crops.

While the position-based approach is hard to extend to further aspects but easy to adapt on different crops, the sensor-based approach can be extended to aspects of the adapted treatment of different weed types. Furthermore, the generation of weed maps to distinguish different types of weeds, maps for plant properties like biomass, etc. can be extracted from the sensor-based algorithm which can in a later step be used for adapted control of bigger machines in the field. These maps are currently mainly performed by UAVs (Stroppiana et al. 2018 ). This concept could be a new basis for subtle disease detection in the field (Görlich et al. 2021 ). Robots have been shown to be able to adopt important tasks currently performed by trained workers or the farmer itself. Nevertheless, its deployment still is not fully autonomously productive and needs surveillance and a well-designed application scenario where field requirements must be adapted to robots which makes an extensive use difficult (Albiero et al. 2022 ). Currently, these robots show promising technology but evaluation studies to quantify the weed effect, the area efficiency, or limitations due to soil properties, climate and environmental factors are still not available. Future development will show that parts of the daily farm work will be done by robots in a way that is different from concepts today. The research outlook and motivation will still be to develop an “All-In-One” farm robot, which combines all necessary tasks from seeding, field management, plant protection, and harvest (Fig.  1 ).

A similar outlook for farmers is given for spaceborne monitoring since the European Space Agency’s (ESA) Copernicus program launched their satellites SENTINEL-2A in June 2015 and SENTINEL-2B in March 2017. Besides environmental monitoring and vegetation observation, they enable the monitoring of crop diseases and pests. The SENTINEL satellite sensors have a sophisticated resolution of up to 10 m per pixel and a spectral range from 442–2200 nm with a resolution of 12 spectral bands. Free data access is possible using different commercial software as well as with no-charge browser solutions like the EO Browser by the ESA ( https://www.sentinel-hub.com ). For some plant diseases and pests, the image time span is critical and short-term applications in the field cannot be conducted, which is currently the main drawback of the free satellite data available. This is because the image frequency depends on the revisit frequency of each single SENTINEL-2 satellite, which is 10 days and in the combined constellation 5 days. In addition, cloud cover might block the field of interest during the imaging. Nevertheless, for retrospective field assessments, as well as research investigations and plant breeding processes, spectral images from satellites are a real benefit to map landscapes with relevant crop and cultivation parameters, identify vulnerable spots, assess the vegetation period or conduct measures for future precision field management (Silva 2021 , Segarra et al. 2020 ). Future satellite programs such as Landsat NeXt will likely unlock new applications and research directions (National Aeronautics and Space Administration, 2021).

One of these applications could be an extension of projects trying to improve the protection of water bodies from unwanted plant protection chemical run-off. Farmers of Germany and Norway have already access to the H 2 Ot-Spotmanager ( http://synops.julius-kuehn.de ). It calculates the environmental risk for waterbodies and their living organisms based on updated satellite, weather, and chemical data. Such applications show the manifold opportunities of satellite data even with a resolution that cannot represent a single plant. Plenty of commercial field management programs which use satellite data are already available (e.g., Xarvio Field Manager, 365 FarmNet, FarmERP, Farmlogs, Agworld, AgriWebb). In these programs, farmers give access to their field data or their whole field index. These data are combined with weather and satellite data to give the farmer a complete overview and information (e.g., about plant nutrition, water status, plant health status, and necessary protection) around the growth of their crop. Robots and satellite applications entail a large potential for plant protection as these machines integrate optical sensors for monitoring, highly trained and efficient models for detection and they are able to carry different actuators for adapted applications in the field (Balafoutis et al. 2020 ). In combination with weather-based forecasting systems and information platforms/databases (e.g., EPPO Global Database, ISIP, Animal and Plant Health Service (USDA)) machines could be trained soon to generate computer-based solutions and consulting before and during the vegetation season.

As highlighted in this review, the achievements in digital plant pathology are great, but the potential is even greater. To exploit the full potential, the state-of-the-art must be regularly questioned while new challenges need to be defined and solved (Fig.  1 ). Because pathosystems can be very specific and complex, existing techniques must be critically evaluated and calibrated according to each pathosystems details. Generalized frameworks and models are necessary, which are intuitive and accessible for the farmer. To develop generalized models, a global database with spectral disease and plant spectra, could be a great foundation. An example of such a database from another field is the TRY plant trait database ( www.try-db.org ; Kattge et al. 2020 ). Challenges of such a collection of spectral data could involve having a standardized approach to clean and upload data (Paulus and Mahlein 2020 ). Access to the database must be simple, contributions should be acknowledged, data storage must be sustainable, and data curation for years or decades should be funded. Also, linking sensor type, ambient conditions and other necessary metadata to the uploaded dataset should be a requirement. Currently, many publications are presenting analysis pipelines on few, isolated databases (e.g., Plant Village Data; https://www.kaggle.com/emmarex/plantdisease ) that have no relation to the complex situation experience in the field. Often, algorithms are not new, and the biological interpretation is missing. This, however, should be the prerequisite for novel publications in the realm of digital plant pathology. Interlocking the complex aspects of phytopathology, sensors, and machine learning is needed. A global database could help to capture and disentangle this complexity.

Unfortunately, concepts using optical sensors for plant disease detection in the field are not yet established, or are still in its infancy, for them to be integrated into decision-supporting solutions. While many calls exist for conducting interdisciplinary research to solve the remaining and persistent challenges, guidance in the form of funding or academic positions for this type of research rarely exists (Heim et al. 2019b ; Bock et al. 2020 ; Brown et al. 2015 ). Therefore, we suggest the following research and action steps support the development and application of digital plant pathology in the field:

• Conferences for the development of an international spectral database (ISD) of the global main crops.
• Obligation to provide (image) data for publication and inclusion into the ISD.
• Investigation of the influence of abiotic factors on collected data.
• A standardized framework for the collection of remote sensing data, including metadata on ambient and sensor conditions, and sufficient ground reference data.
• Investigation into scale independence of spectral information.
• Enable machine communication (sensor, platform, computer, analysis software) on a common software basis.
• Proof of concept for field applications based on ecological and economic standards.

Digital plant pathology, as well as the whole digitalization of agriculture, will change farmers' identity, skills, and work (Klerkx et al. 2019 ; Zolkin et al. 2021 ). To effectively implement digital technologies in practical agriculture, educated and trained farmers, as well as local consultants, with a commitment to new digital technologies, are required. Until this adaptation happens, these technologies will only become available to farmers via companies, start-ups, or the advisory services. Independent of the transfer from research to application, the common goal must be highly precise plant protection measures and higher performativity without affecting the sustainability of the natural environment.

Acknowledgements

This study was partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy—EXC 2070-390732324. The authors thank Anne-Katrin Mahlein and all other colleagues involved in research for helpful discussion and collaboration

Author contributions

MTK and RHJH have contributed equally to this manuscript. MTK, RHJM and SP wrote the manuscript. IG revised the final manuscript. KG and AB helped to develop the topic. All authors read and approved the final manuscript.

Declarations

The authors declare that they have no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Matheus Thomas Kuska and René H. J. Heim have contributed equally to this work.

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• Open access
• Published: 26 May 2021

Global challenges facing plant pathology: multidisciplinary approaches to meet the food security and environmental challenges in the mid-twenty-first century

• Michael Jeger   ORCID: orcid.org/0000-0003-2734-8122 1 ,
• Robert Beresford 2 ,
• Clive Bock 3 ,
• Nathan Brown 4 ,
• Adrian Fox 5 ,
• Adrian Newton 6 ,
• Antonio Vicent 7 ,
• Xiangming Xu 8 &
• Jonathan Yuen 9  

CABI Agriculture and Bioscience volume  2 , Article number:  20 ( 2021 ) Cite this article

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The discipline of plant pathology has an expanding remit requiring a multi-faceted, interdisciplinary approach to capture the complexity of interactions for any given disease, disease complex or syndrome. This review discussed recent developments in plant pathology research and identifies some key issues that, we anticipate, must be faced to meet the food security and environmental challenges that will arise over coming decades. In meeting these issues, the challenge in turn is for the plant pathology community to respond by contributing to a wider forum for multidisciplinary research, recognising that impact will depend not just on advances in the plant pathology discipline alone, but on interactions more broadly with other agricultural and ecological sciences, and with the needs of national and global policies and regulation. A challenge more readily met once plant pathologists again gather physically at international meetings and return to the professional and social encounters that are fertile grounds for developing new ideas and forging collaborative approaches both within plant pathology and with other disciplines. In this review we emphasise, in particular: the multidisciplinary links between plant pathology and other disciplines; disease management, including precision agriculture, plant growth and development, and decision analysis and disease risk; the development and use of new and novel plant protection chemicals; new ways of exploiting host genetic diversity including host resistance deployment; a new perspective on biological control and microbial interactions; advances in surveillance and detection technologies; invasion of exotic and re-emerging plant pathogens; and the consequences of climate change affecting all aspects of agriculture, the environment, and their interactions. We draw conclusions in each of these areas, but in reaching forward over the next few decades, these inevitably lead to further research questions rather than solutions to the challenges we anticipate.

Plant pathology is the scientific study of plant diseases and pathogenic agents Footnote 1 across a diverse range of environments including agricultural and horticultural crops, amenity and forest trees, and natural plant communities. The broad range of pathogens and affected hosts can lead to specialisation within the science and has the potential to constrain a shared approach and dialogue among plant pathologists, especially with respect to disease management. There are examples where a shared approach has been shown necessary and productive, such as with soil-borne diseases with interactions among multiple agents, e.g., fungi and nematodes (Zhang et al. 2020 ); or, where the involvement of vectors in the transmission and spread of viruses, bacteria and fungi is seen as a common feature that can be exploited in disease management. However, much published research is dominated by the single pathogen, single crop, single disease paradigm. Within plant pathology, a paradigm shift may be required to view pathogens and diseases as components of ecosystems, including farming systems, and to describe their epidemiology and management more quantitatively. Current concepts in plant pathology which have provided much of our understanding of when and how diseases develop, need to be re-visited and integrated into a wider quantitative framework that can be applied across disciplines. Basic tenets of plant pathology, such as the ‘disease triangle’ (Agrios 2005 ), the ‘disease life cycle’ (De Wolf and Isard 2007 ) and Koch’s postulates (Agrios 2005 ) need to be examined, and possibly modified or replaced with concepts more suited to integration with crop agronomy and ecosystem functions and services in the search for more robust disease management strategies and applications.

A problem in plant pathology, perversely, is the term ‘pathogen’. The pathogenic phase of a microbial parasite is only one aspect of a plant–microbe interaction that should always be qualified by spatial and temporal parameters. So, plant pathology should be the study of all the factors that influence the interactions between plants and microbes and their outcomes both spatial and temporal, and how these can be managed towards a benign or beneficial state, as with rhizobia. This would mean widening the approach to genetic diversity to bring in susceptibility as well as resistance determinants and would re-visit non-host resistance as well: the ‘trophic space’ of the plant–microbe interaction (Newton et al. 2010a , b ). The discussion above focusses on disease, i.e., the symptomatic expression of microbial infection. However, under-represented in our evaluation of the effects of microbes is asymptomatic infection. Such infection or colonization can have a range of effects from unrecognized source of inoculum, through induction of defence responses that may have short-term cost through to long-term benefits (Atkins et al. 2010 ; Newton et al. 2010a , b ). Asymptomatic microbes can also be endophytes conferring benefits on their hosts such as resistance to herbivores exploited in highly beneficial ways in grass cultivars developed in New Zealand for example, or to induce effective resistance against fungal pathogens in cereals as well as promoting crop growth (Gill et al. 2016 ). However, the challenge is how to bring together the detailed molecular and the whole crop systems in practice. This can only be achieved by a multi-disciplinary approach.

There are also multidisciplinary Footnote 2 links and interactions with other plant protection disciplines, plant breeding, crop management, food safety and security, phytosanitary regulation, soil science, and plant and environmental health more generally, although these links have often been implicit rather than explicit in research endeavours. All these interactions fall within the broad term of systems biology where understanding the interactions among components is crucial. Over the next decades, global issues relating to climate change and international biosecurity associated with increasing trade and air travel will lead to new challenges in all areas of agriculture and the environment, including the management of plant diseases and their societal impact. Hence, a forum for communication of research findings related to these global issues, from the molecular and ecological interactions among plants, pathogens, other microbiota, and vectors, to aetiology and epidemiology of disease in field populations and diverse landscapes, is essential if these challenges are to be met. We recognise of course that there are many challenges purely within the ‘traditional’ plant pathology domain, and acknowledge their importance, but these can be considered and presented elsewhere. Accordingly, this review is structured according to key topics (Fig.  1 ) where the scope for taking a multidisciplinary approach to global issues is most apparent. For each topic we outline the challenges and opportunities that will arise over the next decades, and in some cases propose solutions.

Schematic showing topics within plant pathology where multidisciplinary approaches in research have been developed but need further implementation as described in this review. The two arms of the schematics are shown for ease of presentation. Cross links between the two are present and for some there has been wider involvement of farmers, landholders, regulators, and other participants, but in all areas there will be a need for improvement to meet future challenges as discussed in this review

The interface between plant pathology, crop protection and other disciplines in agricultural and environmental sciences

Plant pathology shares an interface with all disciplines in agricultural and environmental sciences. This interface is fundamental in meeting the challenges of food security and environmental stewardship in the twenty-first century (Fig.  2 ). In crop protection, there is an increasing need for pathologists and entomologists to work together in an integrated approach to pest management (Jactel et al. 2020 ). Approaches to pest Footnote 3 risk analysis in plant health are already benefitting from integration across disciplines; especially concerning formal phytosanitary systems as specified under the International Plant Protection Convention (IPPC), or more informal seed certification schemes in support of sustainable crop production. Agronomic practices of irrigation, soil management and sanitation have long been known to be key components in plant disease management strategies for agricultural and horticultural crops (Jeger 2005 ). Canopy and soil moisture management, tillage, soil amendments, sowing seasons, and crops sequences, are major cultural practices in crop protection resulting in disease escape, inoculum reduction and microclimate modification, but are often considered as single practices rather than an integrated whole. Agronomic practices often change due to a range of social and environmental factors. Shortage of labour has led to a change in rice cultivation from transplanting to direct seeding (Savary et al. 2005 ). When direct seeding was combined with poor management due to water shortages, the pest profiles changed due to direct effects and interactions between the two factors, with some pest impacts increased and others decreased. This analysis was further expanded to include fertilizer treatment and availability of land as factors of agronomic change, with again both positive and negative impacts on the pest profile (Savary et al. 2011 ). The concept of a pest profile was subsequently expanded to that of a crop profile including many agronomic components as well as the pest profile and impacts (Savary et al. 2017 ). At the interface with social sciences, cultural practices for disease control often require shared responsibility and need a collective approach to disease management among growers to be implemented at a regional level.

Venn diagram representing the multidisciplinary challenges faced as plant pathology addresses burgeoning issues of food security and environmental stewardship in the twenty-first century. The ring represents the first challenge: the interface between plant pathology, crop protection and other disciplines. Each of the surrounding seven circles represents one of the remaining major challenge areas identified and discussed in the article. All seven of challenges channel results of research into food security and environmental stewardship. The seven challenge areas are themselves interconnected. Sections “ Disease surveillance, detection and diagnosis ” and “ Disease management ” have additional, specific areas of challenge discussed herein and indicated by the accompanying smallest circles in this figure

There is a wide range globally of farming systems that vary according to climate, topography, and geo-political and socio-economic factors. A distinction has often been made between low-input and intensive farming systems but perhaps there has been insufficient critical examination of this distinction and there may be disease management solutions that apply to both. Non-chemical interventions for disease control are of increasing importance in the context of low-input agricultural systems, where the use of fungicides and insecticides is limited, but there are also incentives for reduced chemical interventions in intensive systems. Non-chemical methods are paramount for disease control in organic farming systems, but the fungicides allowed are often less effective than their synthetic alternatives (Tamm and Holb 2015 ). Strategies for use of non-chemical methods are more complex to implement and a greater research effort into these is required. Low-input agricultural systems apply both in less-developed countries, where external resources and farm inputs are not always readily available, and in developed countries, where more sustainable crop production is driven by consumer preferences and environmental policies, rather than the lack of resources. The demand for access to synthetic pesticides and fertilizers can be expected to continue in the short-term until more durable management methods are found to be effective.

It is possible that information-based intensification of crop production may contribute to a greater sustainability and more effective use of resources. Such intensification is equally applicable irrespective of the level of external inputs to the system, although new approaches will be needed to ensure information is accessible to resource-poor farmers in less developed countries and regions. Examples of intensification can be found in what has been termed precision agriculture, a greater appreciation of the role of plant growth and development in disease epidemiology, and greater contribution to the theory and practice of Integrated Pest Management (IPM), ultimately leading to a recognition that Integrated Crop Management (ICM) should be the goal. In practice, IPM is a term often used by practitioners in crop protection when it should be ICM.

The interfaces among the topics considered in this review (Fig.  1 ) and the ways in which they contribute to food security and environmental challenges of the twenty-first century are shown in a Venn diagram (Fig.  2 ).

• Disease management

The purpose of disease management is to maintain and improve plant health and production, whether in cropped, semi-natural, or non-cropped systems. Pathogens and pests reduce yield, both quantitatively and qualitatively, in crop production causing economic losses and threatening food security. Yield reductions have been documented for five globally important crops, wheat, rice, maize, potato, and soybean (Savary et al. 2019 ), in the order of 10–40% with most impact reported in resource-poor regions with fast-growing populations. Future projections of such analysis can serve to prioritize crop health management in the coming decades, both regionally and globally. The impacts of plant disease on ecosystem services in both cropped and non-cropped systems needs to be considered in any yield loss inventory (Cheatham et al. 2009 ; Avelino et al. 2018 ).

The historical reliance on highly effective fungicides for disease control, and, to some extent, insecticides for virus vector control, has shaped the way plant pathologists think about plant diseases and their management. It has not always been the case that innovations and developments in disease control have depended on advances in epidemiological understanding (Jeger 2004 ). In the future, as microbial biocontrol agents, plant defense elicitors and possible microbiome manipulations become available, and reduce the reliance on synthetic pesticides, a new understanding of the role of plant pathogens and the diseases they cause in whole cropping systems and in the provision of ecosystem services will be needed. This will require greater integration of plant pathology concepts and methodologies with those of other disciplines so that the processes driving disease epidemics and our ability to deliver new disease management systems can be conceived in a wider context.

For plant diseases, resistance is widely recognized in the context of plant breeding and molecular host–pathogen interaction as referring to resistance genes or quantitative traits, but less in terms of how resistant cultivars should be deployed in cropping systems. In terms of crop management, the term ‘vulnerability’ conveys better the varying impact that environmental, agronomic and host phenology factors have on disease in host plant populations. Comparing host plant resistance in terms of symptom expression in a mature conservation agriculture context between inversion and non-inversion tillage, the difference can be an order of magnitude. However, the underlying factors can be multiple, including inoculum (quality and quantity), microbial/microbiome interactions, microclimatic, and nutrients (both micro- and macro-). Diversity in the system at all levels is clearly a major component of vulnerability in practice, but how can this be quantified in determining how all the components of diversity interact? The relevant literature is mostly based on ecological principles, but specific aspects of exploiting host diversity such as the use of cultivar mixtures in space and time to reduce fungicide input and mitigate fungicide resistance development represents a practical implementation in ICM (Kristoffersen et al. 2020 ).

Precision agriculture

Precision agriculture presents opportunities for all farming systems. The advent of precision farming technologies coupled with remote sensing methods opens entire new fields of research, where the performance of cultural practices for plant disease management can be addressed (Kitchen 2008 ). With the aid of artificial intelligence and machine learning algorithms, these technologies may allow an integration of the spatiotemporal dynamics of disease at the farm level with environmental data, soil characteristics and agronomic practices, leading to more targeted disease control interventions. Precision agriculture (including horticulture) has the potential to deliver the transformation of farm productivity needed to meet future global food security and climate change challenges through information-intensive monitoring technologies and crop models that can predict productivity and the analysis of farming system performance. To achieve this for whole farming systems, it will be crucial to incorporate disease, pest and weed constraints into current process-based plant growth models such as the Agricultural Production Systems Simulator (APSIM; Keating et al. 2003 ). Research outputs in precision agriculture and plant pathology are still largely confined to discipline-based core journals, so their interactions need a greater level of exploration and exploitation.

Plant growth and development

Despite the major advances in understanding the molecular underpinning of plant-pathogen interactions, there has been very little work on whole plant physiology, growth and development affecting disease in crop populations. The sensitivity of the cropping system to pathogen challenge needs to be tested during all stages of crop growth and phenology, so that their roles in crop loss and inoculum production can be more clearly understood. Disease susceptibility of different plant organs often varies during plant development, even in hosts considered genetically susceptible to a pathogen, e.g., for apple canker ( Neonectria ditissima ), stems and fruit become infected (Xu and Robinson 2010 ) but leaves do not. An organ’s susceptibility may change during development as is seen in fungal fruit rots that express symptoms during fruit ripening, e.g., Colletotrichum spp. in apple (Grammen et al. 2019 ), Botrytis cinerea in tomato (Blanco-Ulate et al. 2016 ) and in grape (Mundy and Beresford 2007 ). Pea stipules under attack by Mycosphaerella pinodes ( Didymella pinodes ) increase in susceptibility as they age (Richard et al. 2012 ). Our perception of host susceptibility is understandably oriented towards the plant organ on which economic production depends. Disease may affect the organ of interest directly, e.g., leaf area destruction in essential oil crops, or indirectly, e.g., the depression of grain yield caused by destruction of photosynthetic leaf area in cereal foliar pathogens. However, disease on other organs may contribute inoculum to an epidemic, e.g., leaf spots caused by Pseudomonas syringae pv. actinidae contribute to kiwifruit bacterial canker (Froud et al. 2015 ), and, for apple scab, leaf lesions of Venturia inaequalis contribute both conidial and ascosporic inoculum to infections that render fruit unmarketable (Bowen et al. 2011 ). It is noteworthy that much of this research has been focused on individual pathogen/disease combinations with little consideration to how the consortium of pathogens changes during plant growth and development and impacts productivity.

The crop leaf canopy is crucial for both plant growth and disease development. Process-based growth models, including APSIM, generate virtual leaf canopies that simulate production and partition carbohydrates. This provides an opportunity to incorporate disease (and pest) processes as stressors inhibiting photosynthate accumulation. For many diseases, the leaf canopy is not uniformly susceptible to infection throughout its seasonal development and ontogenic resistance (Develey-Rivière and Galiana 2007 ) to biotrophic or hemi-biotrophic pathogens occurs in many host species. This change from susceptibility in young leaves to resistance in mature leaves occurs in many important diseases, including V. inaequalis on apple (Li and Xu 2008 ) and in several powdery mildews, including Uncinula necator ( Erysiphe necator ) on grapevine (Ficke et al. 2003 ) and Podosphaera aphanis on strawberry (Asalf et al. 2014 ). Because ontogenic resistance restricts infection to actively growing shoots or in some cases to senescing plant organs, host growth determines the timing of seasonal epidemics. In many temperate crops, the season’s leaf canopy is established during spring, which makes this an important time for the onset of disease epidemics. Only a few studies of epidemic dynamics mediated by host growth have been made, e.g., for apple scab (Beresford et al. 2004 ) and myrtle rust, caused by Austropuccinia psidii (Tessmann et al. 2001 ; Beresford et al. 2020 ). The architecture of the crop canopy is an important consideration, not only for issues relating to the microclimate and disease susceptibility, but in designing new crop varieties with desirable agronomic and crop protection traits (Costes et al. 2013 ).

Decision analysis and disease risk

One pest management issue needing more attention is related in part to the risk attitudes of decision makers and how this is related to how well predictive systems work or don’t work. Although a theoretical approach based on Bayesian analysis has been developed for incorporating risk attitudes into evidence-based decision systems (Yuen and Hughes 2002 ; Nayak et al. 2018 ), there have been few empirical studies. What do farmers and decision makers want in a predictive system? The sensitivity (the proportion of positive predictions that are correct) and specificity (the proportion negative predictions that are correct) of the predictions made by different decision systems may be a critical issue, especially if the target groups (which could also vary) basically want a perfect sensitivity of 100%. This contrasts with attitudes to weather forecasts where target groups would generally accept the prediction, even when it is qualified by a certain probability bound. Why should one demand more of one prediction, compared to another, especially when weather forecasts may play an important role in disease prediction? A framework has been proposed which combines risk perception, the subjective probability of disease occurrence and the impact of incorrect decisions may explain the failure in adoption of predictive schemes (McRoberts et al. 2011 ). Ultimately it is likely to remain the case over the next decades that decisions on plant disease risk and management will be based on incomplete data and analyses that are subject to high levels of uncertainty (McRoberts et al. 2019 ).

This is partly connected to risk attitudes that plant pathologists don’t always consider and could benefit from the insights and expertise of both socio-economists (Sauter et al. 2015 ) and social psychologists (Mankad 2016 ). The interface between the plant diseases and their control, and why farmers (or growers, or advisors) make certain decisions is an issue that should be examined more thoroughly (Gent et al. 2013 ), with the input of social scientists. Indeed, it has been argued that purely technical assessments of disease risk may not provide an adequate understanding of the decisions made by growers and landowners, and those in the policy domain. Hence, more account needs to be taken of intuitive and normative social responses of individuals and organizations with possibly conflicting interests in managing plant disease (Mills et al. 2011 ; Ilbery et al. 2012 ). Equally the development of future public institutions concerned with plant health should be aligned with the needs, values and preferences of the communities affected by plant disease (Garcia-Figuera et al. 2021 ).

Challenges in the development and use of new plant protection chemicals

The application of conventional plant protection chemicals remains the dominant control method for many plant diseases worldwide, especially for fungal diseases. There is a need for research to assess the threat to sufficiency of global food production that may result from the widespread withdrawal of crop protection chemicals as active ingredients are banned worldwide for human health and environmental reasons. The tradeoffs between yield losses and potential environmental and health impacts of chemical disease control agents will require more attention, and not solely by plant pathologists, but also by related disciplines, crop producers and policy makers. This should be combined with analysis of the social factors associated with the adoption of new pest and disease management methods (Milne et al. 2015 ). In the last several decades many plant disease control chemicals have been banned or are now highly regulated for very limited use, an example being methyl bromide (Schneider et al. 2003 ). More recently there has been concern over the indirect effects of neonicotinoid pesticides applied as seed treatments on non-target organisms, including beneficial insects and bees. This has led to temporary bans on their use in some countries. Compared to field applied pesticides, there was much less knowledge among farmers about what active ingredients were being applied as seed treatments across a wide range of arable crops (Hitaj et al. 2020 ).

The cost of developing new products is high, and the regulatory hurdles continue to be stringent. But there are other practical issues that arise among both existing and novel disease control products, including loss of efficacy due to the pathogen developing resistance to the chemistry. Indeed, fungicide resistance is a recurrent issue in the management of numerous plant diseases (Brent and Holloman 2007 ; Stevenson et al. 2019 ), with more recently resistance developing to multiple modes of action in the same pathogen (Weber 2011 ). Thus, fungicide resistance and the need to manage existing chemistries becomes complex and challenging (Brent and Holloman 2007 ). Regulation, leading to a loss of many of the old, often broad-spectrum chemistries, and the cost of developing new products that are most often single site modes of action, is a harbinger that the impact of loss of fungicide sensitivity will likely increase. But can novel approaches be developed to reduce resistance breakdown in single-site mode of action pesticides?

Thus, a challenge continues to be prolonging and maximizing the effective life of fungicides through an understanding of resistance fitness penalties (Hawkins and Fraaije 2018 ), advances in management approaches and tools, and in modeling various characteristics of resistance to better enable its management (Bosch et al. 2014 ). Here again we can be informed by a better understanding of the application of ecology to managing fungicides as recently demonstrated by the effect conferred by the heterogeneity of cultivar mixtures to mitigate against selection for fungicide resistance, specifically Septoria leaf blotch resistance in wheat (Kristoffersen et al. 2020 ), but the principles of exploiting diversity for resilience are likely to apply more widely.

A further major challenge is to develop effective new chemistries that have minimal impact on the environment and health yet have durable efficacy due to a low risk of resistance development (Hollomon 2015 ). Perhaps based on advances at the intersection of chemistry, biochemistry, molecular biology and genomics, ‘designer’ fungicides may be developed that address some of these issues, but again how best to deploy any new products will require evaluation during their development not as an afterthought. In the absence of appropriate plant genetic resistance, an ongoing challenge for the plant pathology community will be to continue to develop knowledge of pesticide resistance in populations of plant pathogens, understand mechanisms of that resistance as early as possible, and applying this knowledge to develop pesticide management programs that maximize efficacy while minimizing the risk of resistance developing. Another area of crop protection that has critical trade-off issues is the development and exploitation of resistance elicitors. Such products are generally not toxic but prime or activate plant defences thereby enhancing resistance. They are often not as efficacious as conventional biocidal crop protectants but in the era of ICM these are beginning to find their niche and we need to better understand how they can be developed as an asset to sustainable crop protection (Walters et al. 2014 ). Although the use of these resistance elicitors or more generally priming chemicals in agriculture is limited by their insufficient control and variable efficacy when used alone, ways of combining them with other components to optimise their potential in the context of ICM is gaining evidence (Bruce et al. 2017 ; Yassin et al. 2021 ).

New approaches to exploit genetic diversity: how best to deploy host resistance

Ultimately, durable host plant disease resistance is perceived as the goal in disease management that would minimize the need for use of conventional plant protection chemistries. Much knowledge and many of the tools needed to introduce resistance to cultivated genotypes exists, including through conventional breeding approaches, and by biotechnology-based approaches of gene editing and gene silencing. However, these are not trivial approaches, and each takes many years to develop within a framework limited by existing knowledge, technology and legal or regulatory issues. A practical problem arises from the time consuming and labour intensive demands of host phenotyping. To overcome this constraint, new techniques involving optical sensors, artificial intelligence and machine learning have been proposed, “digital phenotyping” (Mahlein et al. 2019 ), which may have field application. The issue of durability is also strongly linked not just to the molecular and physiological responses to pathogen challenge, but to how resistance is deployed in host populations. Many questions still need attention. For example, from an evolutionary perspective, the costs of virulence may constrain the range of host genotypes a particular pathogen strain can adapt to and has implications for breeding for durable resistance and epidemiology (Laine and Barrѐs 2013 ).

Conventional breeding methods have been pivotal to obtain new plant genotypes with disease resistance traits to withstand epidemics, with major opportunities arising with new gene editing technologies (Pixley et al. 2019 ). However, while most plant breeding research as published in discipline-based journals focuses on the quantification of disease resistance and to elucidate its molecular basis, the interplay between resistant/tolerant cultivars, cultural practices and climate conditions for disease management has been little explored. Although genetic markers for selection and breeding are routinely made available, can resistance be developed in time to manage emerging and invasive threats such as by targeting generic stress-response mechanisms (Newton et al. 2012 )? The challenges in plant breeding are especially striking when considering tree crops which have slow growth and a long generation time (Boshier and Buggs 2015 ; Kelly et al. 2020 ; Showalter et al. 2020 ; Stocks et al. 2017 ). In these cases, the evaluation of the durability or resistance traits will take several decades.

Much neglected in the breeding of elite cultivars selected for performance under high input agronomy is their suitability for use in intercropping, but the resilience conferred by the diversity of intercropping is gaining considerable interest. In the former, resistance must be durable under the intense selection of a monoculture bred for self-competition. With intercropping, the range of epidemiological interactions available to a spatiotemporally diverse crop canopy can all be brought to bear on disease management. However, are the mechanisms sufficiently understood, how they work together, their genetic basis and whether classical approaches such as calculating general and specific combining ability are appropriate? We know that we can better exploit major gene resistance even within species by deploying genes in mixtures but how do we best exploit all types of resistance within and between species within intercropping (Fabre et al. 2015 ) and therefore how should we breed for such resistance? Or, to return to the earlier point, how best to breed crops for reduced vulnerability to pathogens.

Promises and challenges for holobiont and microbiome research: an expanded perspective on microbial interactions, biological control, and disease management

Across the animal and plant kingdoms, the ‘holobiont’ consists of a host and its associated microbiota, the ‘microbiome’ (Pitlik and Koren 2017 ). Disease can then be considered as arising from a perturbation of a healthy microbiome. The plant microbiome has received much attention over the last decade (Mercado-Blanco et al. 2018 ; Baldrian 2019 ; Vonaesch et al. 2018 ), in both food crops (Ding et al. 2019 ) and forest trees (Feau and Hamelin 2017 ; Koskella et al. 2017 ; Pinho et al. 2020 ). It has long been recognized that plant-associated microbiomes, the phytobiome (e.g., the phyllosphere, endophyte, and rhizosphere microbiome), will affect directly and/or indirectly disease development (Glaeser et al. 2019 ; Martin et al. 2019 ; Rabiey et al. 2019 ; Tsolakidou et al. 2019 ). Nevertheless, in much plant pathological research the focus remains on the action of individual pathogens in relation to climatic conditions and management methods on a susceptible host is usually the subject of study, but ignoring the complex resident microbiome in which a given disease is developing (Denman et al. 2018 ; Doonan et al. 2020 ). Microbial interactions including specific pathogens, such as in biocontrol research, are often studied yet the interactions have a high level of complexity (Zicca et al. 2020 ). However, in many microbial interaction studies, these are often restricted to interactions involving 2–3 organisms. The simplistic approach results primarily from: (1) a lack of efficient means of profiling the plant-associated microbiome, and (2) a lack of overall understanding of a pathogen’s biology and epidemiology of the resulting disease, which can hinder the development of disease management strategies.

Recent advances in nucleic acid sequencing technologies have enabled the profiling of the microbiome of environmental samples. There are a growing number of published studies adding to our understanding of the effect of biotic and abiotic (including cultural management) factors on the plant-associated microbiome (Deakin et al. 2018 ; Peiffer et al. 2013 ; Schreiter et al. 2014 ; Wang et al. 2019 ). However, there remain difficulties including how to incorporate microbiome-related factors into plant disease epidemiological and management research. Firstly, how could the microbiome of a given sample be represented? Although many Operational Taxonomic Units (OTUs; often > 1000) are found in a sample, usually < 100 of the OTUs account for most sequence reads. Many OTUs with very low counts could be a result of sequence errors, but how confidently can these minor OTUs be excluded from sample microbiome representation? Secondly, an OTU table from amplicon sequences only represents the relative frequencies of OTUs in each sample, when absolute microbial biomass/counts may possibly be more important. It is possible that qPCR could be used to estimate the total microbial biomass via the generic fungal ITS and bacterial 16S primers (e.g., Tilston et al. 2018 ). Alternatively, each environmental sample may be spiked with a known amount of a synthetic DNA fragment to estimate absolute abundance (Tkacz et al. 2018 ). Thirdly, there is not yet the reliable means to manipulate the plant-associated microbiome with predictable outcomes (Sessitsch et al. 2019 ). Therefore, it would be hard to ensure a homogeneous microbiome background whilst studying disease development. Finally, given the complexity of the microbiome, with many OTUs uncultivable as well as unidentifiable, it is difficult to conduct hypothesis-driven research on the interaction of a specific pathogen with one or more components of microbiomes. Solutions to these problems in the application of microbiome studies present challenges to be addressed. An ambitious objective in phytobiome research, integrating all factors which affect plant function, would be to “estimate the potential relative contribution of different components of the phytobiome to plant health, as well as the potential and risk of modifying each in the near future” (Bell et al. 2019 ). This objective effectively means taking a systems-level approach in which the microbial, environmental, macro-organism and plant management components are integrated with a potential role for generic crop modelling (Lamichhane et al. 2020 ).

Disease surveillance, detection, and diagnosis

A key challenge over the next decades is to develop tools and methodology that enable the rapid detection of disease outbreaks, especially those associated with novel or emerging plant pathogens, and the accurate diagnosis of the causal agents. This challenge can be met at the large scale by new surveillance techniques using monitoring networks (Hartmann et al. 2018 ), and at the small scale by new detection and diagnostic technologies made possible by new sequencing methodology. The fundamental challenge arises from integrating these technologies and approaches. Because of the change in scale from the molecular to the region, country or even continent, the challenge can only be met by a systems approach with an international dimension. Plant biosecurity is usually considered using the plant biosecurity continuum concept with trade surveillance and controls considered pre-border, at the border, and post-border (Gordh and McKirdy 2014 ). In an increasingly connected world this effectively brings the need for a global surveillance system, highlighting the need to access skills and technologies to increase baseline knowledge of pest and pathogen presence across the globe (MacDiarmid et al. 2013 ; Carvajal-Yepes et al. 2019 ).

Issues for surveillance

Automatic sensor technology has been used to supplement and, in some cases, replace conventional disease assessments and has the potential to be applied more widely under field conditions, whether in precision agriculture or host phenotyping (Mahlein et al. 2019 ; Bock et al. 2020 ). In that sense it enables surveillance of disease development at the field scale. One of the biggest challenges for any surveillance program occurs after a pest or pathogen has arrived and established in a new region or country. In these situations, early detection is vital for successful management and eradication, but it is often hard to achieve in large heterogeneous landscapes where host distributions may not be represented accurately, and causal agents may be novel. New technologies and working methods have great potential to improve detection. Remote sensing and scales from aerial imaging to satellite technology can identify the actions of individual pathogens in monocultures; for Xylella fastidiosa this can be achieved before symptoms are visible on the ground (Zarco-Tejada et al. 2018 ; Heim et al. 2019 ). When host distributions are more heterogeneous, for example, in native woodland, disease detection from imagery becomes a more complicated problem. Identifying host species accurately is a necessary precursor to disease detection and is not always possible, however the extent and locations of disturbances can still be accurately documented (Cohen et al. 2016 ).

A key challenge is early detection in the wider environment, how best to deploy technologies and integrate automated remote sensing with mass participation through stakeholder engagement. Technology, in the form of smart phone applications and web-based reporting, is also improving the ability of volunteers and land users to report signs of ill health in their crops and the wider environment. Such reports are best described as passive surveillance and often occur through citizen science programs (Dobson et al. 2020 ) but can also be made through a wider range of land users who report observations to the relevant authority (Meentemeyer et al. 2015 ; Brown et al. 2020 ). Observations collected through passive surveillance can provide vitally important first detections of new and emerging diseases but represent a challenge to analysts and modelers due to their unstructured nature. They are made when an observer both notices something of concern and decides to make a report, as such records can be described as messy and may contain biases that affect the accuracy of predictions (August et al. 2020 ). Techniques to identify and correct for biases in citizen science programs and the data bases that result have been reported (Baker et al 2019 ). Citizen science programs can be designed to improve detection and increase awareness of plant health problems, an example of this process can be found in Colorado, US, where volunteers are asked to help map host (ash tree) distributions in advance of pest arrival ( Agrilus planipennis ) (Alexander et al. 2020 ). The role of land user and stake holder participation in the detection and management of plant health issues is a key area for future research (Brown et al. 2017 ; Milne et al. 2020 ) as their decisions can influence the outcome of management and eradication programs: an issue found to be of considerable importance for tree crops in the UK (Marzano et al. 2015 , 2019 ).

Issues for diagnostics

Early in the high-throughput sequencing (HTS) era, MacDiarmid et al. ( 2013 ) made three recommendations for the challenges posed by this new diagnostics technology, especially concerning plant viruses: (1) countries should baseline what pests were present in their territory and had a burden of proof for demonstrating risk of novel findings before implementing plant health action; (2) viruses not associated with disease should get special designation; and (3) there was a need for funding in areas of basic research such as virus ecology and to develop host-virus pathogenicity prediction tools. Several years later the biggest challenge for HTS diagnostics arguably still lies in its own success, in terms of the numerous novel and unusual pathogens (mainly viruses) being discovered (Villamor et al. 2019 ). HTS is revolutionizing the diagnostics workflow in the laboratory with applications ranging from single sample diagnostics and answering decades old questions of disease etiology through to population studies and supporting plant trade by being able to declare the material free from pathogens (Maree et al. 2018 ). However, as a technology which has been exploited for more than a decade in virus research, the routine adoption of this technology, especially in frontline diagnostic applications and with fungal and bacterial pathogens, has lagged. This is due to key blockers such as costs, validation, processing and handling large volumes of data, and probably most crucially, how to handle the risk assessment of novel discoveries (Olmos et al. 2018 ; Massart et al. 2017 ). In this respect, the double-edged sword of HTS based diagnostics is very much worth exploring, as the other plant pathology disciplines are shortly to realize the issues the virologists have been wrestling with over the last decade. The main challenge remains determining the link between the viruses inferred from sequence data and the symptoms of disease which led to the sample being sequenced, and consequently allow inferences to be made on the potential impact of these pathogens (Fox 2020 ).

The complications of interpreting HTS data in frontline diagnostics go beyond causation and feeding this forward into assessing the risk of the new, unusual, and mixed infections is now being encountered with a degree of regularity. Massart et al. ( 2017 ) put forward a framework for evaluating the risk of new virus detections, but this was very much based on singular detections and would be difficult to apply to complex infections or polymicrobial diseases. For example, the suggested approach of infectious clone work as a means of overcoming causation questions may not be practical given the volume of new findings and the potential for complex infection interactions. The added complication of confounding factors such as environmental influences and timing of infection to symptom development also requires a more holistic approach. A predictive approach was suggested by Babayan et al. ( 2018 ) for mammalian arboviruses, but there would be challenges in applying this to plant viruses. Not least of these is the relative lack of information on host range and transmission for many plant viruses by comparison to their mammalian-infecting counterparts. Also spanning this whole area is the lack of centralized information on plant viruses given the neglect of online resources such as the Plant Virus Online VIDE-database and the AAB Descriptions of plant viruses, leaving the resources such as CABI datasheets and the EPPO Global Database as the main sources of information, which whilst useful lack search functionality for some hosts and their distribution, and would hamper any attempts to start gathering this type of data for analysis.

There is also the challenge of tying together the discoveries of the pre-sequencing and post-sequencing eras. There are many examples of viruses and ‘virus-like- agents’ discovered in the mid-to-late twentieth century which had been described based on their biological, serological and physiochemical properties for which no sequence data exist. Many of these pathogens have been included in plant health regulations around the globe, and some are even recognized as species. However, with the increased use of non-targeted sequence-based detection, combined with the limited resources available for time consuming and costly biological characterization work there are examples of ‘rediscovery’, such as the case of plantain virus X and Actinidia virus X, two synonymous viruses discovered at opposite sides of the globe, over 30 years apart, and in unrelated hosts (Hammond et al. 2020 ), a case study pulling together two recognized species discovered 30+ years apart (pre and post sequencing eras) where the conclusions being drawn on the risk of the latter were incorrect due to lack of knowledge on the first non-sequenced report. Historic isolate collections can be an invaluable resource. Such historic isolates allow support for risk assessment though baselining for presence and host range and for informing evolutionary studies (Jones et al. 2020 ).

On top of these issues, there is the question of scale, which links detection and diagnostic technology with the surveillance issues noted above. As the technology gets applied for area and landscape scale studies, these issues will be further compounded by a lack of sample-specific contextual data, which must be considered during experimental design or surveillance schemes. Whilst HTS technologies offer unparalleled diagnostic potential, for these approaches to be routinely applied issues such as provision of validation data to demonstrate the performance characteristics of the platforms and open sharing of data and research coordination need to be added to the outstanding items on the original list discussed by MacDiarmid et al. ( 2013 ). Can pathogen risk factors be identified from HTS inferred sequences?

Exotic and re-emerging pathogens

A current and burgeoning challenge for the discipline of plant pathology is the introduction and spread of pathogens to new locations, and emergence or re-emergence of new pathogens against a background of a changing climate (Sumner 2003 ; Garbelotto and Pautasso 2012 ; Gottwald et al. 2019 ; Carvajal-Yepes et al. 2019 ). The rate of transfer of plant material both as traded commodity and as living material for planting or breeding purposes has accelerated over the last several decades. As a result, exotic plant diseases have become more prevalent and problematic in agricultural and natural systems throughout the world, to the point of developing recovery plans to potential risks posed by some pathogens (McRoberts et al. 2016 ). From a plant health perspective there is a gap between non-native pathogens intercepted on a regular basis and those which go on to establish in a region. This gap is complex and poorly understood with a range of influencing factors including climate and host suitability, but also understanding pathways e.g., fruit going to market as opposed to seeds or plants for planting. For a challenge—better understanding of this gap may allow for better targeting of resources to the pathogen/trade pathways presenting the greatest risk?

The trend in exotic plant disease is likely to increase as international travel, trade, and societal unrest (Hulme 2009 ) continue to provide opportunities for pathogen dispersal. Identifying new outbreaks is challenging as often the disease may already be quite widespread when first identified, for example, with HLB ( Candidatus Liberibacter asiaticus) in Florida in 2005 (Halbert 2005 ; Gottwald et al. 2007 ). Tracking spread also requires a rapid and effective response to be effective. Quarantine, using sentinel trap plants, molecular diagnostics or canines to detect an organism may all currently be used, but the challenge to develop novel tools that may be part of early detection, warning and management to increase the effectiveness of dealing with exotic diseases is a challenge (Gottwald et al. 2019 ). Also managing a disease once identified may require complex coordination of resources and the support of local communities, and the environment, that can otherwise derail eradication efforts as happened with citrus canker in Florida (Gottwald et al. 2002 ; Gochez et al. 2020 ). Research to establish the pathogen spread, survival and dispersal of propagules using existing and novel tools will be paramount to minimize impact of exotic pathogens. A challenge is to develop effective predictive models that will aid early detection and perhaps allow placement and integration of early detection systems (Pautasso 2013 ). Understanding the genetic basis for any changes in populations of exotic or re-emerging pathogens will similarly be critical to identify any reasons for changes in patterns of the epidemic and thus responding appropriately (Grunwald and Goss 2011 ). Additionally, risks are posed by unregulated trade and ‘exotic’ hosts. Current formal phytosanitary systems pick up pathogens on formal trade routes either before or after entry (VanDersal 2007 ; Fox and Mumford 2017 ). However, new trade routes and patterns, informal trade especially through internet sales may be more difficult to police even with novel diagnostics (Giltrap et al. 2009 ; Kaminski et al. 2012 ; Fox et al. 2019 ). Can methods (both regulatory and technological) be developed that can encompass the needs of more conventional trade, and these more recent and difficult-to-track trade networks?

If not detected prior to introduction, eradication of exotic diseases should remain the goal and developing effective eradication programs will continue to be challenging against an ever more mobile society, transferring more diverse material that may not be strictly regulated, and by individuals who may have conflicting personal or political priorities or interests. Regulatory measures play a major role in preventing those introductions as well as controlling established outbreaks through eradication or containment. The implementation of regulatory measures is often associated with trade disputes and social concerns, sometimes leading to delayed or even halted interventions (Marzano et al. 2015 ). Exploring the socio-economic dimensions of regulatory disease control would be a productive cross-discipline exercise. The plant pathology discipline faces challenges to provide the tools to the regulatory agencies to detect the pathogen and/or exotic and re-emerging plant diseases at the earliest stage possible, and subsequently to develop rapidly, more effective eradiation and disease management plans that are achievable within the socio-economic limitations.

Climate change

Climate change has already, and increasingly, will affect the prevalence and frequency of different plant diseases across a spectrum of important staple and specialty crops, and in natural ecosystems (Garrett 2008 ; Chakraborty et al. 2008 ; Chakraborty and Newton 2011 ; Pautasso et al. 2012 ; Elad and Pertot 2014 ; Burdon and Zhan. 2020 ). Predicting the future impacts of climate change on plant disease is not a simple matter. Unexpected and possibly unpredictable impacts may arise due to the interactions of climate change with other factors, including shifts in host range, changes in agricultural intensification, introductions of exotic pathogens, and genetic events (Corredor-Moreno and Saunders 2020 ). The factors that affect climate change impact can be categorized in terms of ‘risk mitigation’ and ‘risk enhancement’ (Fig.  2 in Chakraborty and Newton, 2011 ) recognizing that mitigating and enhancing influences are the result of complex interactions among these ‘remediating’ and ‘enhancing’ influences. Multiple components of these interactions (pathogens, crops, vectors, natural enemies, microbiome) are influenced by climatic variables in different ways. The challenge is to determine the relative importance of the biological processes and the key climatic influences together to predict the likely impact of climate change on production systems in time and space. An increasing number of climate related epidemics have been characterized. The example of Phytophthora ramorum (cause of sudden oak death) in North America and Europe being particularly well described in terms of invasive nature and likely anticipated spread due to a changing climate. Factors associated with both the host pathogen interaction and availability of inoculum may affect spread and incidence of the disease. Warmer, drier, or wetter conditions may all influence the host and/or pathogen, or the interaction in ways that may increase, or reduce the effect of the disease on a plant host. Phoma stem canker of oilseed rape in the UK was increased due to earlier epidemic development due to milder seasons. Interactions between climate warming and pathogen biology is likely to produce differential effects on diseases in different climatic zones. For Austropuccina psidii (myrtle rust), an invasive species of subtropical origin, climate warming is expected to increase disease in temperate areas through increased annual frequency of conditions favourable for the pathogen infection cycle. Conversely, in the tropics, longer periods above the maximum temperature for infection and latent development may reduce the risk of disease (Beresford et al. 2020 ).

Furthermore, encompassing pollutants changes in composition of the atmosphere other than just carbon dioxide appears to also impact pathogens, as with the change in relative abundance of the cereal pathogens Phaeosphaeria nodorum and Mycosphaerella graminicola ( Zymoseptoria tritici ) in the UK attributed to sulphur dioxide (Fitt et al. 2011 ). This study emphasized the value of long-term data sets in interpreting past trends in pathogen prevalence (Jeger and Pautasso 2008 ) and in host abundance in natural plant communities (Salama et al. 2012 ). At the landscape scale, deposition of atmospheric pollutants including nitrogen and sulphur, have also been identified as potential predisposing factors underlying oak decline (Brown et al. 2018 ), with nitrogen imbalance and differing abundance of nitrogen cycling microorganisms observed at the tree level (Scarlett et al., 2020 ).

The economic and environmental impact of specific diseases in different regions will likely shift over the coming decades and the range of pathogens able to infect a host species will similarly shift, which will be a challenge to monitor. This shift will present challenges to the producers of crops and stewards of natural ecosystems as existing or novel methods for disease management will need to be transferred or developed by plant pathologists and implemented against a background of stringent disease control regulation. How effective will some management strategies such as biological control be in a shifting climate? Research will be challenged to better understand how climate shifts will affect existing pathogen life cycles and survival, host susceptibilities and host pathogen interactions. A continuing challenge to phytosanitary organizations will be the requirement for novel tools to address changes in presence and abundance of pathogens. This includes identifying threats now posed by pathogens from exotic locales due to shifts in climate in other areas where conditions may become conducive to invasion.

Understanding the underlying condition of host plants, especially in natural environments is therefore a crucial component to understanding pest and disease impacts and stresses the contextual information necessary for effective disease surveillance. The effect of drought and other disturbances on forested ecosystems has been dramatic, with wide scale dieback and decline (Choat et al. 2012 , 2018 ; Millar and Stephenson 2015 ; Seidl et al. 2020 ). The role of pest and pathogens in tree mortality in low rainfall conditions is poorly understood (Stovall et al. 2020 ) and integrated research and monitoring is needed to reveal the extent of the affected areas and the mechanisms that underpin mortality (Hartmann et al. 2018 ). Changes to silvicultural systems have been proposed as a means of climate change adaptation (Bradford and Bell 2017 ).

Conclusions

This review has been wide-ranging and identified some key challenges and opportunities for plant pathology research over the next few decades by emphasizing the inter- and cross-disciplinary links with other disciplines in the agricultural and environmental sciences. We acknowledge that the scale and change in research emphasis we recommend will require changes in the current model for research funding, especially where immediate solutions to pressing problems are required by research funders. Also, the structure of academic research institutions and the types of incentives and recognition systems that are often in place counter-indicate the change in emphasis we envisage.

Despite this important qualification, we have attempted to pull together these links across the topics which form the structure of this review in Fig.  3 . To re-iterate, this is not to say that conceptual, methodological, and technological developments within the discipline do not also present their own challenges and opportunities, but that is not the emphasis here. The key conclusions we draw are:

Changes in cropping systems and wild plant communities will be multifactorial, meaning that the causes and consequences of plant diseases in these systems must be seen from a whole system perspective.

Interactions of pathogen life stages with varying organ susceptibility during plant development need to be understood as part of the whole system. Integration of disease models into crop growth models offers a way to quantify how pathogen-crop interactions, including yield effects and inoculum production, and could pave the way for quantitative understanding of more complex interactions between host plants, their pathogens and other microbiome components.

Canopy (and root system) architecture will be a greater consideration in designing and breeding new varieties for agronomic objectives; the implications must be accounted for in the development of new disease and pest management strategies.

There are different genetic, spatial, and temporal dimensions to diversity and their potential exploitation in crop and environmental management. This applies not just in host and pathogen populations, but in soils and associated microbiota, tillage, and the use of trap plantings in crop protection.

New chemistries as well as the more effective exploitation of chemical induced resistance agents or resistance elicitors may have the potential to offer more benign and sustainable disease control interventions. How will these chemistries interact with plant responses to multiple pathogens and pests?

The microbiome concept has revolutionized the ways in which microbial interactions with plants and in the environment are perceived but may lead to a switch away from the ideal of hypothesis-driven research. The potential relevance to disease management is clear but needs to be realized.

Improved remote sensing technologies are being developed that can be used at different scales. Similarly, more informal systems of mass surveillance, including citizen science, are gaining traction because of the savings in costs and associated resources. Methods need to be developed to integrating these two approaches to surveillance.

Specificity and sensitivity of new sequencing diagnostic techniques raise new problems in interpretation. This has been most apparent with plant viruses but will be faced by other disciplines within plant pathology. The use of diagnostic facilities must be linked with the contextual information obtained from surveillance.

There is every indication that trade in plants will continue at a global scale, and that human mobility will increase due to business, leisure, migration, and social disruption. The challenges to disease and pest management will accordingly increase. Options to meet these challenges will include placement of sentinel plantings for surveillance or pathogen detection systems in trade networks but will require a continuing and strengthened international cooperation.

Climate change, mitigation and adaptation have received much attention in relation to crop diseases and pests. An area that has received less attention is the effects on wild plant communities whether in relation to the impacts of novel pathogen encounters or through their underlying responses to climate change.

Schematic showing how the interlocking of different strands of multidisciplinary research in plant pathology should develop to meet the cropping, food security and environmental challenges of the coming decades. The diagram shows the continuum between cropped and non-cropped systems. Genetic and plant chemistry research will contribute from seed to mature plant performance. An understanding and management of host–pathogen interactions and epidemiology will benefit from research across the continuum. Climate change and the global trade in commodities will drive the introduction and spread of exotic pathogens into both cropped and non-cropped systems with the concomitant need for improved and linked surveillance and diagnostic systems. In all areas of research there should be a role for social scientists and other concerned participants in research scoping, planning and implementation

Availability of data and materials

Not applicable.

For the purposes of this review, we consider pathogenic agents to include fungi, oomycetes, bacteria and phytoplasmas, viruses and viroids, and macroparasites including parasitic plants and nematodes. We note that the pathogenic phase may only form part of their life history.

For this term, we include interaction a cross disciplines and at the inter face between disciplines.

‘Pest’ can refer collectively to ‘pathogens’, ‘invertebrates’, and ‘weeds’ in the international biosecurity context, whereas within the individual disciplines of plant pathology, entomology and weed science, the respective individual terms are more often used and pest tends to be reserved for invertebrate pests.

Abbreviations

Association of applied biologists

Agricultural production systems simulator

European and Mediterranean Plant Protection Organization

Huanglongbing disease of citrus

High throughput sequencing

Integrated crop management

Internal transcribed spacer

Integrated pest management

International Plant Protection Convention

Operational taxonomic unit

Virus identification data exchange

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Jeger, M., Beresford, R., Bock, C. et al. Global challenges facing plant pathology: multidisciplinary approaches to meet the food security and environmental challenges in the mid-twenty-first century. CABI Agric Biosci 2 , 20 (2021). https://doi.org/10.1186/s43170-021-00042-x

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Exploring the Intersection of Plant Pathology and Sustainable Development

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Anna Brugger is a leading expert in the study of plant diseases and their impact on the environment. In this interview, we will delve into her passion for plant pathology, her research, and how it relates to Sustainable Development Goals. We will also discuss the role of plant pathology in biodiversity conservation and sustainable use.

What inspired you to pursue a career in the field of plant pathology, and what do you find most rewarding about your work? 

Various studies calculate crop loss due to harmful organisms such as fungi or insects at 30 to 40% if no crop protection products are used. For some crops, the loss would be close to 100%, and for others much lower. These figures and the demands of consumers for perfect products that are always available, coupled with a growing world population and climate extremes, result in an area of tension that has fascinated me since I was a student. That my field of research therefore never loses its necessity and relevance was one of the main reasons why it fascinated me so much. Today, I work in applied research, and in my current work, I find collaboration with farmers particularly rewarding, in addition to teaching.

Can you tell us about your current research and how it relates to Sustainable Development Goals?

My current research focuses on arable farming and how crop protection products and fertilizers can be used most effectively. In many crops, farmers rely on the use of crop protection products to ensure the profitability of growing that crop. This is done according to the principle of integrated pest management, which means that the use of chemical pesticides should only take place when preventive or non-chemical measures do not provide sufficient and economic protection for the crop. Many of my studies start with these preventive measures and investigate questions such as: How can crop rotation reduce disease incidence? Where can the use of mechanical weed control and undersowing reduce herbicide use? Can pests be deflected with the help of early flowering plants? 

So this is always about the second goal of the Sustainable Development Goals, growing enough healthy food for the world's population while promoting sustainable agriculture. At the same time, we are also touching on Goal 15, which aims to stop the loss of biodiversity.

How do you see plant pathology impacting biodiversity conservation and sustainable use?

The combination of Goal 2 and Goal 15 of the Sustainable Development Goals poses a major challenge. On the one hand, food security must be ensured, on the other hand, the decline of biodiversity has to be stopped. Intensive agriculture and urbanization are changing the habitats of many species’ groups, often reducing the diversity of natural habitats and agro-ecosystems. This has a negative impact on biodiversity. So, we are faced with the big challenge of producing enough food while promoting biodiversity. Starting in 2024 there is a new requirement in Switzerland that all farms must comply with: To promote biodiversity on arable land, various extensive areas must be cultivated on at least 3.5% of the arable land. In this way, not only insects are to be promoted, but also, for example, brown hares and skylarks. At the same time, I see integrated pest management with its motto "as much as needed, as little as possible" as the most important element to combine plant protection and biodiversity conservation. However, this requires a good knowledge of plant pathology, which emphasizes the importance of training farmers. Only when plant diseases and pests are correctly identified can targeted measures be taken against them. In the future, optical sensors that make it possible to precisely localize and identify plant diseases can also play an important role. Thus, the site-specific use of plant protection products can become an important component of integrated plant protection in the future.

Can you explain the role of plant pathology in promoting sustainable agricultural practices?

To promote sustainable agricultural practices, a sound knowledge of plant development and plant diseases must be available at the practical level. First, it must be known how plant diseases can be prevented. In addition to the choice of varieties, these preventive measures include, for example, soil cultivation and crop rotation. In addition to recognizing plant diseases or insect pests, farmers must also be aware of the stage of plant development at which they can occur. Finally, farmers must be roughly familiar with the modes of action of plant protection products so that the correct choice of product can be made, and resistance can be prevented.  This knowledge must be generated from research and tested in applied research. In the next step, it must be communicated to farmers in teaching.

How do you see your research contributing to protecting and restoring ecosystems and habitats?

Many of my studies aim to reduce or optimize the use of crop protection products while maintaining the quantity and quality of agricultural production. This is to promote existing habitats and biodiversity. For example, I am investigating whether an undersowing in sunflower not only reduces herbicide use but can also increase sunflower yields due to the nitrogen-fixing property of the undersowing. Another research aspect is if an early-flowering border in canola can distract insects, which in turn reduces insecticide use. We also implement policy measures such as the previously mentioned extensive areas on arable land and show how these areas should be managed and what their effect can be. Since this is applied research, an important part of my work is communication with farmers. This takes place through articles about our new trial results but also through guided tours on our trial plots. 

How do you see plant pathology, and sustainable development intersecting in the future?

Plant pathology research has changed dramatically in recent years, and precision agriculture has become increasingly prominent. I see great potential in this area so that plant pathology and sustainable production do not contradict each other. With the help of precision agriculture, the use of pesticides and fertilizers can be reduced, and productivity remains constant. However, I see the use of sensors to detect plant diseases or monitor weather data only as a piece of the puzzle. It can only ever be combined with the basic principle of integrated crop protection and, above all, the knowledge of the farmers. I believe that this holistic approach will allow sustainable development of agriculture if precision agriculture succeeds in making the step from research into practice.

What advice would you give young women interested in pursuing a career in plant pathology?

Knowledge about plant diseases is only one important component of this research area. Knowledge about plants should not be neglected and, above all, experience in production is crucial. My advice is not to neglect this area, allowing this exciting field to be viewed holistically at the level of the pathogen, the plant, and the interaction.

Visit our SDG 15 hub for selected research about Life on Land.

About the author.

Anna Brugger (born 1990) grew up in Germany and graduated with a bachelor's degree in biology from Friedrich Schiller University in Jena and a master's degree in microbiology from the University of Bonn. There she specialized in plant diseases and followed up her master's studies with a PhD under the supervision of Prof. Dr. Anne-Katrin Mahlein. In her dissertation, she investigated the detection of plant diseases using optical sensors and focused on the UV range. Since October 2020, Anna has been working in Switzerland at the Arenenberg Education and Advisory Center of the State of Thurgau. Here she focuses on applied research in arable farming and teaches young trainees. She is an associate editor of Journal of Plant Diseases and Protection.

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Plant Pathology and Microbiology News

Shim named head of Department of Plant Pathology and Microbiology

Won Bo Shim, Ph.D., has been named head of the Department of Plant Pathology and Microbiology in the Texas A&M College of Agriculture and Life Sciences.

Anderson hired to address crop disease in the High Plains

Nolan Anderson, Ph.D., a new   Texas A&M AgriLife Research and Texas A&M AgriLife Extension Service plant pathologist in Amarillo, didn’t set out to be a plant pathologist.

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Plant Pathology Research in China: A Centennial View

Edited by: Prof. Jun Liu and Prof. Xiaorong Tao

Plant pathology as a discipline has been developed over one and a half century. We have benefited from the research achievements of plant pathology, by which we are able to deploy knowledge to make a better and healthier life. The teaching of plant pathology in China can be traced back to 1905, the year that the Agriculture College of the Imperial University of Peking was founded. Plant pathology was officially accepted as a subdiscipline until 1921 when the Department of Plant Diseases and Pests was established in the National Southeast University. The Department of Plant Pathology was first established in the Jinling University (Private University of Nanking) in 1927. After that, more and more colleges and universities accepted plant pathology as a major discipline. Looking back on the past 100 years, research community of plant pathology in China has grown up from a small group of people to a large force. Knowledge acquired from plant pathology research has been securing sustainable agriculture in China. Here, we organize a special issue to celebrate the centenary of the formal establishment of Plant Pathology in China.

This series was published in  Phytopathology Research .  

Identification of ceRNA-vsiRNA-mRNA network for exploring the mechanism underlying pathogenesis of sugarcane mosaic virus in resistant and susceptible maize inbred lines

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Characterization of mating type, spore killing, and pathogenicity of Fusarium verticillioides populations from maize in China

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Molecular characteristics and pathogenicity of infectious cDNA clones of the Chinese isolates of pea enation mosaic virus 1 and pea enation mosaic virus 2

The pea enation mosaic disease (PEMD) causes significant yield losses worldwide. PEMD is caused by two taxonomically unrelated but symbiotic viruses, pea enation mosaic virus 1 (PEMV-1) and pea enation mosaic ...

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Feed the Future Global Biotech Potato Partnership

Global biotech potato partnership attends australasian plant pathology society conference.

March 28, 2024

Global Biotech Potato Partnership attends Australasian Plant Pathology Society Conference.

The Global Biotech Potato Partnership participated in the 2023 Australasian Plant Pathology Society Conference held in Adelaide, Australia November 20-24.

The theme of the event, "Change and Adaptation" highlighted the adaptation of plant pathogens and also how new innovations are being used for disease control.

Dr. Phil Wharton and Dr. Most Mahbuba Begum presented a poster on their publication, "Genotypic characterization of Phytophthora infestans populations in Bangladesh" which details the results of a large-scale survey of potato fields in the main potato-growing divisions of Bangladesh examining genotypic diversity of P. infestans populations.

Dr. Begum is a plant pathologist in the Tuber Crops and Research Center at the Bangladesh Agricultural Research Institute (TCRC-BARI). Dr. Begum leads pathology efforts for the Global Biotech Potato Partnership in Bangladesh.

Dr. Wharton is an Associate Professor a the University of Idaho who studies the biology and host-pathogen interactions of fungal diseases on potatoes and etiology and epidemiology of potato diseases. Wharton serves as the Global Resource Lead on Pathology for the Global Biotech Potato Partnership.

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