In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine

Diseases and pests in agriculture significantly impact crop yield and quality. Downy mildew (Plasmopara viticola) is a particular noteworthy example in grapevines. Traditional detection methods are laborious, subjective and time-consuming. Consequently, a technological solution based on artificial i...

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Detalles Bibliográficos
Autores: Hernández, Inés, Gutiérrez, Salvador, Barrio Fernández, Ignacio, Íñiguez, Rubén, Tardáguila, Javier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/372374
Acceso en línea:http://hdl.handle.net/10261/372374
https://api.elsevier.com/content/abstract/scopus_id/85204873041
Access Level:acceso abierto
Palabra clave:Computer vision
Convolutional neural networks
Digital agriculture
Disease detection
Explainable artificial intelligence
Vision transformers
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spelling In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevineHernández, InésGutiérrez, SalvadorBarrio Fernández, IgnacioÍñiguez, RubénTardáguila, JavierComputer visionConvolutional neural networksDigital agricultureDisease detectionExplainable artificial intelligenceVision transformersDiseases and pests in agriculture significantly impact crop yield and quality. Downy mildew (Plasmopara viticola) is a particular noteworthy example in grapevines. Traditional detection methods are laborious, subjective and time-consuming. Consequently, a technological solution based on artificial intelligence, would provide higher levels of reproducibility and sampling. The aim of this work was to develop an interpretable, automated method for detection and localisation of plant disease symptoms under field conditions. Images of the grapevine canopy were taken in 14 commercial vineyard plots under a range of lightning conditions, including both static and on-the-go settings. The images were processed using a sliding window, classifying sub-images into areas with and without downy mildew. Transfer learning, fine-tuning and data augmentation were employed to automate the classification, comparing convolutional neural networks (CNNs) and vision transformers (ViT). Subsequently, the trained model was integrated into the sliding window to localise regions within the canopy images exhibiting symptoms of downy mildew. Model predictions were interpreted using explainable artificial intelligence (XAI) methods. The EfficientNetV2S model achieved an accuracy of 91 % and an F1-score of 0.92 when classifying image areas and an Intersection over Union (IoU) of 0.83 when locating symptomatic areas. This method showed promising results, enabling automatic and explainable detection and localisation of plant diseases in complex conditions. The straightforward labelling process facilitated adaptation to new conditions, making it suitable for different crops and diseases. Integration into mobile platforms could enhance disease management and reduce the spread of pathogens, making a significant advance in agricultural technology.This work has been developed as part of the project NoPest (Novel Pesticides for a Sustainable Agriculture), which received funding from the European Union Horizon 2020 FET Open program under Grant agreement ID 828940. Inés Hernández and Rubén Iñíguez would like to acknowledge the research funding FPI PhD grants 1150/2020 and 591/2021 by Universidad de La Rioja and Gobierno de La Rioja.Peer reviewedElsevier BVEuropean CommissionUniversidad de La RiojaGobierno de La RiojaHernández, Inés [0000-0003-3093-8238]Gutiérrez, Salvador [0000-0002-8205-9772]Tardáguila, Javier [0000-0002-6639-8723]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/372374https://api.elsevier.com/content/abstract/scopus_id/85204873041reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/HE/828940https://doi.org/10.1016/j.compag.2024.109478Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3723742026-05-22T06:33:51Z
dc.title.none.fl_str_mv In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine
title In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine
spellingShingle In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine
Hernández, Inés
Computer vision
Convolutional neural networks
Digital agriculture
Disease detection
Explainable artificial intelligence
Vision transformers
title_short In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine
title_full In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine
title_fullStr In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine
title_full_unstemmed In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine
title_sort In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine
dc.creator.none.fl_str_mv Hernández, Inés
Gutiérrez, Salvador
Barrio Fernández, Ignacio
Íñiguez, Rubén
Tardáguila, Javier
author Hernández, Inés
author_facet Hernández, Inés
Gutiérrez, Salvador
Barrio Fernández, Ignacio
Íñiguez, Rubén
Tardáguila, Javier
author_role author
author2 Gutiérrez, Salvador
Barrio Fernández, Ignacio
Íñiguez, Rubén
Tardáguila, Javier
author2_role author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Universidad de La Rioja
Gobierno de La Rioja
Hernández, Inés [0000-0003-3093-8238]
Gutiérrez, Salvador [0000-0002-8205-9772]
Tardáguila, Javier [0000-0002-6639-8723]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Computer vision
Convolutional neural networks
Digital agriculture
Disease detection
Explainable artificial intelligence
Vision transformers
topic Computer vision
Convolutional neural networks
Digital agriculture
Disease detection
Explainable artificial intelligence
Vision transformers
description Diseases and pests in agriculture significantly impact crop yield and quality. Downy mildew (Plasmopara viticola) is a particular noteworthy example in grapevines. Traditional detection methods are laborious, subjective and time-consuming. Consequently, a technological solution based on artificial intelligence, would provide higher levels of reproducibility and sampling. The aim of this work was to develop an interpretable, automated method for detection and localisation of plant disease symptoms under field conditions. Images of the grapevine canopy were taken in 14 commercial vineyard plots under a range of lightning conditions, including both static and on-the-go settings. The images were processed using a sliding window, classifying sub-images into areas with and without downy mildew. Transfer learning, fine-tuning and data augmentation were employed to automate the classification, comparing convolutional neural networks (CNNs) and vision transformers (ViT). Subsequently, the trained model was integrated into the sliding window to localise regions within the canopy images exhibiting symptoms of downy mildew. Model predictions were interpreted using explainable artificial intelligence (XAI) methods. The EfficientNetV2S model achieved an accuracy of 91 % and an F1-score of 0.92 when classifying image areas and an Intersection over Union (IoU) of 0.83 when locating symptomatic areas. This method showed promising results, enabling automatic and explainable detection and localisation of plant diseases in complex conditions. The straightforward labelling process facilitated adaptation to new conditions, making it suitable for different crops and diseases. Integration into mobile platforms could enhance disease management and reduce the spread of pathogens, making a significant advance in agricultural technology.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/372374
https://api.elsevier.com/content/abstract/scopus_id/85204873041
url http://hdl.handle.net/10261/372374
https://api.elsevier.com/content/abstract/scopus_id/85204873041
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/HE/828940
https://doi.org/10.1016/j.compag.2024.109478
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
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