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De La Fuente L, Navas-Cortés JA, Landa BB. Ten Challenges to Understanding and Managing the Insect-Transmitted, Xylem-Limited Bacterial Pathogen Xylella fastidiosa. PHYTOPATHOLOGY 2024; 114:869-884. [PMID: 38557216 DOI: 10.1094/phyto-12-23-0476-kc] [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: 04/04/2024]
Abstract
An unprecedented plant health emergency in olives has been registered over the last decade in Italy, arguably more severe than what occurred repeatedly in grapes in the United States in the last 140 years. These emergencies are epidemics caused by a stealthy pathogen, the xylem-limited, insect-transmitted bacterium Xylella fastidiosa. Although these epidemics spurred research that answered many questions about the biology and management of this pathogen, many gaps in knowledge remain. For this review, we set out to represent both the U.S. and European perspectives on the most pressing challenges that need to be addressed. These are presented in 10 sections that we hope will stimulate discussion and interdisciplinary research. We reviewed intrinsic problems that arise from the fastidious growth of X. fastidiosa, the lack of specificity for insect transmission, and the economic and social importance of perennial mature woody plant hosts. Epidemiological models and predictions of pathogen establishment and disease expansion, vital for preparedness, are based on very limited data. Most of the current knowledge has been gathered from a few pathosystems, whereas several hundred remain to be studied, probably including those that will become the center of the next epidemic. Unfortunately, aspects of a particular pathosystem are not always transferable to others. We recommend diversification of research topics of both fundamental and applied nature addressing multiple pathosystems. Increasing preparedness through knowledge acquisition is the best strategy to anticipate and manage diseases caused by this pathogen, described as "the most dangerous plant bacterium known worldwide."
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Affiliation(s)
- Leonardo De La Fuente
- Department of Entomology and Plant Pathology, Auburn University, Auburn, AL 36849, U.S.A
| | - Juan A Navas-Cortés
- Department of Crop Protection. Institute for Sustainable Agriculture (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
| | - Blanca B Landa
- Department of Crop Protection. Institute for Sustainable Agriculture (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
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Bai Y, Jin X. Hyperspectral approaches for rapid and spatial plant disease monitoring. TRENDS IN PLANT SCIENCE 2024:S1360-1385(24)00067-0. [PMID: 38584079 DOI: 10.1016/j.tplants.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/14/2024] [Accepted: 03/14/2024] [Indexed: 04/09/2024]
Affiliation(s)
- Yali Bai
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China; State Key Laboratory of Crop Gene Resources and Breeding, Beijing 100081, China; Information Technology Group, Wageningen University and Research, Wageningen, 6706, KN, The Netherlands
| | - Xiuliang Jin
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China; State Key Laboratory of Crop Gene Resources and Breeding, Beijing 100081, China.
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Ispizua Yamati FR, Günder M, Barreto A, Bömer J, Laufer D, Bauckhage C, Mahlein AK. Automatic Scoring of Rhizoctonia Crown and Root Rot Affected Sugar Beet Fields from Orthorectified UAV Images Using Machine Learning. PLANT DISEASE 2024; 108:711-724. [PMID: 37755420 DOI: 10.1094/pdis-04-23-0779-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: 09/28/2023]
Abstract
Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red-green-blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV-orthorectified images was carried out. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation. The custom convolutional neural network trained from scratch together with pretrained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score the plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of random forest and k-nearest neighbors has shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots; therefore, considering the information from individual plants of the plots showed a significant improvement in UAV-based automated monitoring routines.
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Affiliation(s)
| | - Maurice Günder
- Institute for Computer Science III, University of Bonn, DE - 53115 Bonn, Germany
| | - Abel Barreto
- Institute of Sugar Beet Research (IfZ), DE - 37079 Göttingen, Germany
| | - Jonas Bömer
- Institute of Sugar Beet Research (IfZ), DE - 37079 Göttingen, Germany
| | - Daniel Laufer
- Institute of Sugar Beet Research (IfZ), DE - 37079 Göttingen, Germany
| | - Christian Bauckhage
- Institute for Computer Science III, University of Bonn, DE - 53115 Bonn, Germany
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Sapes G, Schroeder L, Scott A, Clark I, Juzwik J, Montgomery RA, Guzmán Q JA, Cavender-Bares J. Mechanistic links between physiology and spectral reflectance enable previsual detection of oak wilt and drought stress. Proc Natl Acad Sci U S A 2024; 121:e2316164121. [PMID: 38315867 PMCID: PMC10873599 DOI: 10.1073/pnas.2316164121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 12/11/2023] [Indexed: 02/07/2024] Open
Abstract
Tree mortality due to global change-including range expansion of invasive pests and pathogens-is a paramount threat to forest ecosystems. Oak forests are among the most prevalent and valuable ecosystems both ecologically and economically in the United States. There is increasing interest in monitoring oak decline and death due to both drought and the oak wilt pathogen (Bretziella fagacearum). We combined anatomical and ecophysiological measurements with spectroscopy at leaf, canopy, and airborne levels to enable differentiation of oak wilt and drought, and detection prior to visible symptom appearance. We performed an outdoor potted experiment with Quercus rubra saplings subjected to drought stress and/or artificially inoculated with the pathogen. Models developed from spectral reflectance accurately predicted ecophysiological indicators of oak wilt and drought decline in both potted and field experiments with naturally grown saplings. Both oak wilt and drought resulted in blocked water transport through xylem conduits. However, oak wilt impaired conduits in localized regions of the xylem due to formation of tyloses instead of emboli. The localized tylose formation resulted in more variable canopy photosynthesis and water content in diseased trees than drought-stressed ones. Reflectance signatures of plant photosynthesis, water content, and cellular damage detected oak wilt and drought 12 d before visual symptoms appeared. Our results show that leaf spectral reflectance models predict ecophysiological processes relevant to detection and differentiation of disease and drought. Coupling spectral models that detect physiological change with spatial information enhances capacity to differentiate plant stress types such as oak wilt and drought.
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Affiliation(s)
- Gerard Sapes
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN55108
- Agronomy Department, University of Florida, Gainesville, FL32611
| | - Lucy Schroeder
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN55108
| | - Allison Scott
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN55108
| | - Isaiah Clark
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN55108
| | - Jennifer Juzwik
- Northern Research Station, United States Department of Agriculture Forest Service, St. Paul, MN55108
| | | | - J. Antonio Guzmán Q
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN55108
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Wang B, Lu H, Jiang S, Gao B. Recent advances of microneedles biosensors for plants. Anal Bioanal Chem 2024; 416:55-69. [PMID: 37872414 DOI: 10.1007/s00216-023-05003-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 10/25/2023]
Abstract
As the lack of plants can affect the energy operation of the entire ecosystem, monitoring and improving the health status of plants is crucial. However, ordinary biosensing platforms lack accuracy and timeliness in monitoring plant growth status. In addition, the prevention and control of plant diseases often involve spraying and administering drugs, which is inefficient and prone to pollution. Microneedles have unique dimensions and shapes, and they have significant advantages as biosensors in the fields of sensing, detection, and drug delivery. Recent evidence suggests that microneedle biosensors can become effective tools for plant diagnosis and treatment. In this review, the comprehensive development of the application of microneedle biosensors in the field of plants is introduced, as well as their manufacturing processes and sensing and detection functions. Furthermore, the application of microneedle biosensors in this field is discussed, and future development directions are proposed.
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Affiliation(s)
- Bingyi Wang
- College of Biotechnology and Pharmaceutical Engineering and School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing, 211816, China
| | - Huihui Lu
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing, 211816, China
| | - Senhao Jiang
- College of Biotechnology and Pharmaceutical Engineering and School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing, 211816, China
| | - Bingbing Gao
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing, 211816, China.
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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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7
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Singh BK, Delgado-Baquerizo M, Egidi E, Guirado E, Leach JE, Liu H, Trivedi P. Climate change impacts on plant pathogens, food security and paths forward. Nat Rev Microbiol 2023; 21:640-656. [PMID: 37131070 PMCID: PMC10153038 DOI: 10.1038/s41579-023-00900-7] [Citation(s) in RCA: 47] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2023] [Indexed: 05/04/2023]
Abstract
Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.
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Affiliation(s)
- Brajesh K Singh
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia.
- Global Centre for Land-Based Innovation, Western Sydney University, Penrith, New South Wales, Australia.
| | - Manuel Delgado-Baquerizo
- Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Sevilla, Spain
- Unidad Asociada CSIC-UPO (BioFun), Universidad Pablo de Olavide, Sevilla, Spain
| | - Eleonora Egidi
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
| | - Emilio Guirado
- Multidisciplinary Institute for Environment Studies 'Ramon Margalef', University of Alicante, Alicante, Spain
| | - Jan E Leach
- Microbiome Newtork and Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - Hongwei Liu
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
| | - Pankaj Trivedi
- Microbiome Newtork and Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
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Carlier A, Dandrifosse S, Dumont B, Mercatoris B. To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status? PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0083. [PMID: 37681000 PMCID: PMC10482323 DOI: 10.34133/plantphenomics.0083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 08/03/2023] [Indexed: 09/09/2023]
Abstract
The utilization of high-throughput in-field phenotyping systems presents new opportunities for evaluating crop stress. However, existing studies have primarily focused on individual stresses, overlooking the fact that crops in field conditions frequently encounter multiple stresses, which can display similar symptoms or interfere with the detection of other stress factors. Therefore, this study aimed to investigate the impact of wheat yellow rust on reflectance measurements and nitrogen status assessment. A multi-sensor mobile platform was utilized to capture RGB and multispectral images throughout a 2-year fertilization-fungicide trial. To identify disease-induced damage, the SegVeg approach, which combines a U-NET architecture and a pixel-wise classifier, was applied to RGB images, generating a mask capable of distinguishing between healthy and damaged areas of the leaves. The observed proportion of damage in the images demonstrated similar effectiveness to visual scoring methods in explaining grain yield. Furthermore, the study discovered that the disease not only affected reflectance through leaf damage but also influenced the reflectance of healthy areas by disrupting the overall nitrogen status of the plants. This emphasizes the importance of incorporating disease impact into reflectance-based decision support tools to account for its effects on spectral data. This effect was successfully mitigated by employing the NDRE vegetation index calculated exclusively from the healthy portions of the leaves or by incorporating the proportion of damage into the model. However, these findings also highlight the necessity for further research specifically addressing the challenges presented by multiple stresses in crop phenotyping.
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Affiliation(s)
- Alexis Carlier
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
| | - Sebastien Dandrifosse
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
| | - Benjamin Dumont
- Plant Sciences, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
| | - Benoît Mercatoris
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
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Galvan FER, Pavlick R, Trolley G, Aggarwal S, Sousa D, Starr C, Forrestel E, Bolton S, Alsina MDM, Dokoozlian N, Gold KM. Scalable Early Detection of Grapevine Viral Infection with Airborne Imaging Spectroscopy. PHYTOPATHOLOGY 2023; 113:1439-1446. [PMID: 37097472 DOI: 10.1094/phyto-01-23-0030-r] [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: 06/19/2023]
Abstract
The U.S. wine and grape industry loses $3B annually due to viral diseases including grapevine leafroll-associated virus complex 3 (GLRaV-3). Current detection methods are labor-intensive and expensive. GLRaV-3 has a latent period in which the vines are infected but do not display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. The NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation was deployed to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA in September 2020. Foliage was removed from the vines as part of mechanical harvest soon after image acquisition. In September of both 2020 and 2021, industry collaborators scouted 317 hectares on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Symptomatic grapevines identified in 2021 were assumed to have been latently infected at the time of image acquisition. Random forest models were trained on a spectroscopic signal of noninfected and GLRaV-3 infected grapevines balanced with synthetic minority oversampling of noninfected and GLRaV-3 infected grapevines. The models were able to differentiate between noninfected and GLRaV-3 infected vines both pre- and postsymptomatically at 1 to 5 m resolution. The best-performing models had 87% accuracy distinguishing between noninfected and asymptomatic vines, and 85% accuracy distinguishing between noninfected and asymptomatic + symptomatic vines. The importance of nonvisible wavelengths suggests that this capacity is driven by disease-induced changes to plant physiology. The results lay a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring in grapevine and other crop species. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
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Affiliation(s)
| | - Ryan Pavlick
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
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Gamon JA, Wang R, Russo SE. Contrasting photoprotective responses of forest trees revealed using PRI light responses sampled with airborne imaging spectrometry. THE NEW PHYTOLOGIST 2023; 238:1318-1332. [PMID: 36658464 DOI: 10.1111/nph.18754] [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: 06/05/2022] [Accepted: 01/16/2023] [Indexed: 06/17/2023]
Abstract
The Photochemical Reflectance Index (PRI) provides an optical indicator of photosynthetic light-use efficiency, photoprotection, and stress in plants. Although PRI can be applied in remote sensing, its interpretation depends on irradiance, which is hard to obtain from satellite or airborne imagery. To quantify forest photoprotective responses remotely, we developed a framework for modeling and interpreting PRI-light responses of individual trees and species using airborne imaging spectrometry coupled with georeferenced forest inventory data from a temperate broad-leaved forest. We derived an irradiance proxy, used hierarchical modeling to analyze PRI-light responses, and developed a framework of physiological interpretations of model parameters as facultative and constitutive components of photoprotection. Photochemical Reflectance Index declined with illumination, and PRI-light relationships varied with landscape position and among tree crowns and species. More sun-exposed foliage had lower intercepts and slopes of the relationship, indicating greater constitutive, but less facultative, photoprotection. We show that tree photoprotective strategies can be quantified at multiple scales using airborne hyperspectral data in structurally complex forests. Our findings and approach have important implications for the remote sensing of forest stress by offering a new way to assess functional diversity through dynamic differences in photoprotection and photosynthetic downregulation and providing previsual indicators of forest stress.
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Affiliation(s)
- John A Gamon
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, 68583-0961, USA
| | - Ran Wang
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, 68583-0961, USA
| | - Sabrina E Russo
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE, 68588-0118, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, 68588-0660, USA
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11
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Xu K, Ye H. Light scattering in stacked mesophyll cells results in similarity characteristic of solar spectral reflectance and transmittance of natural leaves. Sci Rep 2023; 13:4694. [PMID: 36949090 PMCID: PMC10033640 DOI: 10.1038/s41598-023-31718-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 03/16/2023] [Indexed: 03/24/2023] Open
Abstract
Solar spectral reflectance and transmittance of natural leaves exhibit dramatic similarity. To elucidate the formation mechanism and physiological significance, a radiative transfer model was constructed, and the effects of stacked mesophyll cells, chlorophyll content and leaf thickness on the visible light absorptance of the natural leaves were analyzed. Results indicated that light scattering caused by the stacked mesophyll cells is responsible for the similarity. The optical path of visible light in the natural leaves is increased with the scattering process, resulting in that the visible light transmittance is significantly reduced meanwhile the visible light reflectance is at a low level, thus the visible light absorptance tends to a maximum and the absorption of photosynthetically active radiation (PAR) by the natural leaves is significantly enhanced. Interestingly, as two key leaf functional traits affecting the absorption process of PAR, chlorophyll content and leaf thickness of the natural leaves in a certain environment show a convergent behavior, resulting in the high visible light absorptance of the natural leaves, which demonstrates the PAR utilizing strategies of the natural leaves. This work provides a new perspective for revealing the evolutionary processes and ecological strategies of natural leaves, and can be adopted to guide the improvement directions of crop photosynthesis.
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Affiliation(s)
- Kai Xu
- Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei, 230027, People's Republic of China
| | - Hong Ye
- Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei, 230027, People's Republic of China.
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Mesías-Ruiz GA, Pérez-Ortiz M, Dorado J, de Castro AI, Peña JM. Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. FRONTIERS IN PLANT SCIENCE 2023; 14:1143326. [PMID: 37056493 PMCID: PMC10088868 DOI: 10.3389/fpls.2023.1143326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.
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Affiliation(s)
- Gustavo A. Mesías-Ruiz
- Institute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), Madrid, Spain
- Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain
| | - María Pérez-Ortiz
- Centre for Artificial Intelligence, University College London, London, United Kingdom
| | - José Dorado
- Institute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), Madrid, Spain
| | - Ana I. de Castro
- Environment and Agronomy Department, National Institute for Agricultural and Food Research and Technology (INIA), Spanish National Research Council (CSIC), Madrid, Spain
| | - José M. Peña
- Institute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), Madrid, Spain
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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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Physiological and Structural Responses of Olive Leaves Related to Tolerance/Susceptibility to Verticillium dahliae. PLANTS 2022; 11:plants11172302. [PMID: 36079682 PMCID: PMC9459789 DOI: 10.3390/plants11172302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 11/17/2022]
Abstract
Verticillium wilt of olive (VWO), caused by the soil borne fungus Verticillium dahliae, is one of the most relevant diseases affecting this crop worldwide. One of the best VWO management strategies is the use of tolerant cultivars. Scarce information is available about physiological and structural responses in the leaves of olive cultivars displaying different levels of tolerance to VWO. To identify links between this phenotype and variations in functional characteristics of the leaves, this study examined the structural and physiological traits and the correlations among them in different olive varieties. This evaluation was conducted in the presence/absence of V. dahliae. On the one hand, no leaf trait but the area was related to VWO tolerance in the absence of the pathogen. On the other hand, after inoculation, susceptible cultivars showed lower leaf area and higher leaf mass per area and dry matter content. Furthermore, at the physiological level, these plants showed severe symptoms resembling water stress. Analyzing the relationships among physiological and structural traits revealed differences between tolerant and susceptible cultivars both in the absence and in the presence of V. dahliae. These results showed that olive leaves of VWO-tolerant and VWO-susceptible cultivars adopt different strategies to cope with the pathogen.
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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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Kuska MT, Heim RHJ, Geedicke I, Gold KM, Brugger A, Paulus S. Digital plant pathology: a foundation and guide to modern agriculture. JOURNAL OF PLANT DISEASES AND PROTECTION : SCIENTIFIC JOURNAL OF THE GERMAN PHYTOMEDICAL SOCIETY (DPG) 2022; 129:457-468. [PMID: 35502325 PMCID: PMC9046714 DOI: 10.1007/s41348-022-00600-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
- Matheus Thomas Kuska
- North Rhine-Westphalia Chamber of Agriculture, Gartenstraße 11, 50765 Cologne, Germany
| | - René H. J. Heim
- Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany
| | - Ina Geedicke
- Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany
| | - Kaitlin M. Gold
- 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
- Bildungs- und Beratungszentrum Arenenberg, Arenenberg 8, 8268 Salenstein, Switzerland
| | - Stefan Paulus
- Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany
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Abstract
Plant disease threatens the environmental and financial sustainability of crop production, causing $220 billion in annual losses. The dire threat disease poses to modern agriculture demands tools for better detection and monitoring to prevent crop loss and input waste. The nascent discipline of plant disease sensing, or the science of using proximal and/or remote sensing to detect and diagnose disease, offers great promise to extend monitoring to previously unachievable resolutions, a basis to construct multiscale surveillance networks for early warning, alert, and response at low latency, an opportunity to mitigate loss while optimizing protection, and a dynamic new dimension to agricultural systems biology. Despite its revolutionary potential, plant disease sensing remains an underdeveloped discipline, with challenges facing both fundamental study and field application. This article offers a perspective on the current state and future of plant disease sensing, highlights remaining gaps to be filled, and presents a bold vision for the future of global agriculture.
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