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Mahlein AK, Arnal Barbedo JG, Chiang KS, Del Ponte EM, Bock CH. From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management. PHYTOPATHOLOGY 2024:PHYTO01240009PER. [PMID: 38810274 DOI: 10.1094/phyto-01-24-0009-per] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.
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Affiliation(s)
- Anne-Katrin Mahlein
- Institute of Sugar Beet Research (IfZ), Holtenser Landstrasse 77 37079 Göttingen, Germany
| | | | - Kuo-Szu Chiang
- Division of Biometrics, Department of Agronomy, National Chung Hsing University, Taichung, Taiwan
| | - Emerson M Del Ponte
- Departamento de Fitopatologia, Universidade Federal de Viçosa, Viçosa, MG 36570-000, Brazil
| | - Clive H Bock
- U.S. Department of Agriculture-Agricultural Research Service Southeastern Fruit and Tree Nut Research Station, Byron, GA 31008, U.S.A
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2
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Moll L, Giralt N, Planas M, Feliu L, Montesinos E, Bonaterra A, Badosa E. Prunus dulcis response to novel defense elicitor peptides and control of Xylella fastidiosa infections. PLANT CELL REPORTS 2024; 43:190. [PMID: 38976088 PMCID: PMC11231009 DOI: 10.1007/s00299-024-03276-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 06/27/2024] [Indexed: 07/09/2024]
Abstract
KEY MESSAGE New defense elicitor peptides have been identified which control Xylella fastidiosa infections in almond. Xylella fastidiosa is a plant pathogenic bacterium that has been introduced in the European Union (EU), threatening the agricultural economy of relevant Mediterranean crops such as almond (Prunus dulcis). Plant defense elicitor peptides would be promising to manage diseases such as almond leaf scorch, but their effect on the host has not been fully studied. In this work, the response of almond plants to the defense elicitor peptide flg22-NH2 was studied in depth using RNA-seq, confirming the activation of the salicylic acid and abscisic acid pathways. Marker genes related to the response triggered by flg22-NH2 were used to study the effect of the application strategy of the peptide on almond plants and to depict its time course. The application of flg22-NH2 by endotherapy triggered the highest number of upregulated genes, especially at 6 h after the treatment. A library of peptides that includes BP100-flg15, HpaG23, FV7, RIJK2, PIP-1, Pep13, BP16-Pep13, flg15-BP100 and BP16 triggered a stronger defense response in almond plants than flg22-NH2. The best candidate, FV7, when applied by endotherapy on almond plants inoculated with X. fastidiosa, significantly reduced levels of the pathogen and decreased disease symptoms. Therefore, these novel plant defense elicitors are suitable candidates to manage diseases caused by X. fastidiosa, in particular almond leaf scorch.
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Affiliation(s)
- Luis Moll
- Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Núria Giralt
- Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Marta Planas
- LIPPSO, Department of Chemistry, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Lidia Feliu
- LIPPSO, Department of Chemistry, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Emilio Montesinos
- Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Anna Bonaterra
- Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Esther Badosa
- Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV, University of Girona, Campus Montilivi, 17003, Girona, Spain.
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3
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Johnson KA, Brannen PM, Chen C, Bock CH. Visual Assessment of Phony Peach Disease: Evaluating Rater Accuracy and Reliability. PLANT DISEASE 2024; 108:930-940. [PMID: 37822103 DOI: 10.1094/pdis-11-22-2669-re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
Phony peach disease (PPD), found predominantly in central and southern Georgia, is a re-emerging disease caused by Xylella fastidiosa (Xf) subsp. multiplex. Accurate detection and rapid removal of symptomatic trees are crucial to effective disease management. Currently, peach producers rely solely on visual identification of symptoms to confirm PPD, which can be ambiguous if early in development. We compared visual assessment to quantitative PCR (qPCR) for detecting Xf in 'Julyprince' in 2019 and 2020 (JP2019 and JP2020) and in 'Scarletprince' in 2020 (SP2020). With no prior knowledge of qPCR results, all trees in each orchard were assessed by a cohort of five experienced and five inexperienced raters in the morning and afternoon. Visual identification accuracy of PPD was variable, but experienced raters were more accurate when identifying PPD trees. In JP2019, the mean rater accuracy for experienced and inexperienced raters was 0.882 and 0.805, respectively. For JP2020, the mean rater accuracy for experienced and inexperienced raters was 0.914 and 0.816, respectively. For SP2020, the mean rater accuracy for experienced and inexperienced raters was 0.898 and 0.807, respectively. All raters had false positive (FP) and false negative (FN) observations, but experienced raters had significantly lower FN rates compared with the inexperienced group. Almost all raters overestimated the incidence of PPD in the orchards. Reliability of visual assessments was demonstrated as moderate to good, regardless of experience. Further research is needed to develop accurate and reliable methods of detection to aid management of PPD as both FPs and FNs are costly to peach production.
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Affiliation(s)
- Kendall A Johnson
- Department of Plant Pathology, University of Georgia, Athens, GA 30602
| | - Phillip M Brannen
- Department of Plant Pathology, University of Georgia, Athens, GA 30602
| | - Chunxian Chen
- Southeastern Fruit and Tree Nut Research Station, USDA-ARS, Byron, GA 31008
| | - Clive H Bock
- Southeastern Fruit and Tree Nut Research Station, USDA-ARS, Byron, GA 31008
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4
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Wen T, Li JH, Wang Q, Gao YY, Hao GF, Song BA. Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165626. [PMID: 37481085 DOI: 10.1016/j.scitotenv.2023.165626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 07/24/2023]
Abstract
Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.
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Affiliation(s)
- Ting Wen
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Jian-Hong Li
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Qi Wang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, PR China.
| | - Yang-Yang Gao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China.
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China; Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.
| | - Bao-An Song
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
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5
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Berger K, Machwitz M, Kycko M, Kefauver SC, Van Wittenberghe S, Gerhards M, Verrelst J, Atzberger C, van der Tol C, Damm A, Rascher U, Herrmann I, Paz VS, Fahrner S, Pieruschka R, Prikaziuk E, Buchaillot ML, Halabuk A, Celesti M, Koren G, Gormus ET, Rossini M, Foerster M, Siegmann B, Abdelbaki A, Tagliabue G, Hank T, Darvishzadeh R, Aasen H, Garcia M, Pôças I, Bandopadhyay S, Sulis M, Tomelleri E, Rozenstein O, Filchev L, Stancile G, Schlerf M. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. REMOTE SENSING OF ENVIRONMENT 2022; 280:113198. [PMID: 36090616 PMCID: PMC7613382 DOI: 10.1016/j.rse.2022.113198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.
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Affiliation(s)
- Katja Berger
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Miriam Machwitz
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
| | - Marlena Kycko
- Department of Geoinformatics Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warszawa, Poland
| | - Shawn C. Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Shari Van Wittenberghe
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
| | - Max Gerhards
- Earth Observation and Climate Processes, Trier University, 54286 Trier, Germany
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
| | - Clement Atzberger
- Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Str. 82, 1190 Vienna, Austria
| | - Christiaan van der Tol
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Alexander Damm
- Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Uwe Rascher
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Ittai Herrmann
- The Plant Sensing Laboratory, The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel
| | - Veronica Sobejano Paz
- Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Sven Fahrner
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Roland Pieruschka
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Egor Prikaziuk
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Ma. Luisa Buchaillot
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Andrej Halabuk
- Institute of Landscape Ecology, Slovak Academy of Sciences, 814 99 Bratislava, Slovakia
| | - Marco Celesti
- HE Space for ESA - European Space Agency, European Space Research and Technology Centre (ESA-ESTEC), Keplerlaan 1, 2201, AZ Noordwijk, the Netherlands
| | - Gerbrand Koren
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands
| | - Esra Tunc Gormus
- Department of Geomatics Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey
| | - Micol Rossini
- Remote Sensing of Environmental Dynamics Laboratory (LTDA), University of Milano - Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Michael Foerster
- Geoinformation in Environmental Planning Lab, Technische Universität Berlin, 10623 Berlin, Germany
| | - Bastian Siegmann
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Asmaa Abdelbaki
- Earth Observation and Climate Processes, Trier University, 54286 Trier, Germany
| | - Giulia Tagliabue
- Remote Sensing of Environmental Dynamics Laboratory (LTDA), University of Milano - Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Tobias Hank
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Roshanak Darvishzadeh
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Helge Aasen
- Earth Observation and Analysis of Agroecosystems Team, Division Agroecology and Environment, Agroscope, Zurich, Switzerland
- Institute of Agricultural Science, ETH Zürich, Zurich, Switzerland
| | - Monica Garcia
- Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM), ETSIAAB, Universidad Politécnica de Madrid, 28040, Spain
| | - Isabel Pôças
- ForestWISE - Collaborative Laboratory for Integrated Forest & Fire Management, Quinta de Prados, Campus da UTAD, 5001-801 Vila Real, Portugal
| | | | - Mauro Sulis
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
| | - Enrico Tomelleri
- Faculty of Science and Technology, Free University of Bozen/Bolzano, Italy
| | - Offer Rozenstein
- Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization—Volcani Institute, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel
| | - Lachezar Filchev
- Space Research and Technology Institute, Bulgarian Academy of Sciences (SRTI-BAS), Bulgaria
| | - Gheorghe Stancile
- National Meteorological Administration, Building A, Soseaua Bucuresti-Ploiesti 97, 013686 Bucuresti, Romania
| | - Martin Schlerf
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
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Garrett KA, Bebber DP, Etherton BA, Gold KM, Plex Sulá AI, Selvaraj MG. Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation. ANNUAL REVIEW OF PHYTOPATHOLOGY 2022; 60:357-378. [PMID: 35650670 DOI: 10.1146/annurev-phyto-021021-042636] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.
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Affiliation(s)
- K A Garrett
- Plant Pathology Department, University of Florida, Gainesville, Florida, USA;
- Food Systems Institute, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - D P Bebber
- Department of Biosciences, University of Exeter, Exeter, United Kingdom
| | - B A Etherton
- Plant Pathology Department, University of Florida, Gainesville, Florida, USA;
- Food Systems Institute, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - K M Gold
- Plant Pathology and Plant Microbe Biology Section, School of Integrative Plant Sciences, Cornell AgriTech, Cornell University, Geneva, New York, USA
| | - A I Plex Sulá
- Plant Pathology Department, University of Florida, Gainesville, Florida, USA;
- Food Systems Institute, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - M G Selvaraj
- The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), Cali, Colombia
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7
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Anguita-Maeso M, Ares-Yebra A, Haro C, Román-Écija M, Olivares-García C, Costa J, Marco-Noales E, Ferrer A, Navas-Cortés JA, Landa BB. Xylella fastidiosa Infection Reshapes Microbial Composition and Network Associations in the Xylem of Almond Trees. Front Microbiol 2022; 13:866085. [PMID: 35910659 PMCID: PMC9330911 DOI: 10.3389/fmicb.2022.866085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/26/2022] [Indexed: 11/28/2022] Open
Abstract
Xylella fastidiosa represents a major threat to important crops worldwide including almond, citrus, grapevine, and olives. Nowadays, there are no efficient control measures for X. fastidiosa, and the use of preventive measures and host resistance represent the most practical disease management strategies. Research on vessel-associated microorganisms is gaining special interest as an innate natural defense of plants to cope against infection by xylem-inhabiting pathogens. The objective of this research has been to characterize, by next-generation sequencing (NGS) analysis, the microbial communities residing in the xylem sap of almond trees affected by almond leaf scorch disease (ALSD) in a recent X. fastidiosa outbreak occurring in Alicante province, Spain. We also determined community composition changes and network associations occurring between xylem-inhabiting microbial communities and X. fastidiosa. For that, a total of 91 trees with or without ALSD symptoms were selected from a total of eight representative orchards located in five municipalities within the X. fastidiosa-demarcated area. X. fastidiosa infection in each tree was verified by quantitative polymerase chain reaction (qPCR) analysis, with 54% of the trees being tested X. fastidiosa-positive. Globally, Xylella (27.4%), Sphingomonas (13.9%), and Hymenobacter (12.7%) were the most abundant bacterial genera, whereas Diplodia (30.18%), a member of the family Didymellaceae (10.7%), and Aureobasidium (9.9%) were the most predominant fungal taxa. Furthermore, principal coordinate analysis (PCoA) of Bray–Curtis and weighted UniFrac distances differentiated almond xylem bacterial communities mainly according to X. fastidiosa infection, in contrast to fungal community structure that was not closely related to the presence of the pathogen. Similar results were obtained when X. fastidiosa reads were removed from the bacterial data set although the effect was less pronounced. Co-occurrence network analysis revealed negative associations among four amplicon sequence variants (ASVs) assigned to X. fastidiosa with different bacterial ASVs belonging to 1174-901-12, Abditibacterium, Sphingomonas, Methylobacterium–Methylorubrum, Modestobacter, Xylophilus, and a non-identified member of the family Solirubrobacteraceae. Determination of the close-fitting associations between xylem-inhabiting microorganisms and X. fastidiosa may help to reveal specific microbial players associated with the suppression of ALSD under high X. fastidiosa inoculum pressure. These identified microorganisms would be good candidates to be tested in planta, to produce almond plants more resilient to X. fastidiosa infection when inoculated by endotherapy, contributing to suppress ALSD.
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Affiliation(s)
- Manuel Anguita-Maeso
- Department of Crop Protection, Institute for Sustainable Agriculture (IAS), Spanish National Research Council (CSIC), Córdoba, Spain
- *Correspondence: Manuel Anguita-Maeso,
| | - Aitana Ares-Yebra
- Department of Life Sciences, Centre for Functional Ecology, University of Coimbra, Coimbra, Portugal
- Laboratory for Phytopathology, Instituto Pedro Nunes, Coimbra, Portugal
| | - Carmen Haro
- Department of Crop Protection, Institute for Sustainable Agriculture (IAS), Spanish National Research Council (CSIC), Córdoba, Spain
| | - Miguel Román-Écija
- Department of Crop Protection, Institute for Sustainable Agriculture (IAS), Spanish National Research Council (CSIC), Córdoba, Spain
| | - Concepción Olivares-García
- Department of Crop Protection, Institute for Sustainable Agriculture (IAS), Spanish National Research Council (CSIC), Córdoba, Spain
| | - Joana Costa
- Department of Life Sciences, Centre for Functional Ecology, University of Coimbra, Coimbra, Portugal
- Laboratory for Phytopathology, Instituto Pedro Nunes, Coimbra, Portugal
| | - Ester Marco-Noales
- Centro de Protección Vegetal y Biotecnología, Instituto Valenciano de Investigaciones Agrarias, Valencia, Spain
| | - Amparo Ferrer
- Servicio de Sanidad Vegetal, Generalitat Valenciana, Valencia, Spain
| | - Juan A. Navas-Cortés
- Department of Crop Protection, Institute for Sustainable Agriculture (IAS), Spanish National Research Council (CSIC), Córdoba, Spain
| | - Blanca B. Landa
- Department of Crop Protection, Institute for Sustainable Agriculture (IAS), Spanish National Research Council (CSIC), Córdoba, Spain
- Blanca B. Landa,
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8
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Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements. REMOTE SENSING 2021. [DOI: 10.3390/rs13224560] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measurements. Next, based on the characteristic bands (Rλ) associated with leaf anthocyanins (Anth), we determined vegetation indices (VIs) commonly used in plant physiological and biochemical parameter inversion and established a vegetation index (VIc) by utilizing the combination of two arbitrary bands following the construction principles of NDVI, DVI, RVI, and SAVI. Furthermore, we developed classification models based on linear discriminant analysis (LDA) and support vector machine (SVM) in order to distinguish the red leaves from healthy leaves. Finally, we performed UR, MLR, PLSR, PCR, and SVM simulations on Anth based on Rλ, VIs, VIc, and Rλ + VIs + VIc and indirectly estimated the severity of MDMV infection based on the relationship between the reflection spectra and Anth. Distinct from those of the normal leaves, the spectra of red leaves showed strong reflectance characteristics at 640 nm, and SI increased with increasing Anth. Moreover, the accuracy of the two VIc-based classification models was 100%, which is significantly higher than that of the VIs and Rλ-based models. Among the Anth regression models, the accuracy of the MLR model based on Rλ + VIs + VIc was the highest (R2c = 0.85; R2v = 0.74). The developed models could accurately identify MDMV and estimate the severity of its infection, laying the theoretical foundation for large-scale remote sensing-based monitoring of this virus in the future.
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