On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health Assessment

© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Detalles Bibliográficos
Autores: Gálvez, Ana I., Afonso, Frederico, Martínez Heredia, Juana María
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/175224
Acceso en línea:https://hdl.handle.net/11441/175224
https://doi.org/10.3390/rs17132253
Access Level:acceso abierto
Palabra clave:Deep learning
Smart agriculture
Semantic segmentation
Unmanned aerial vehicles
Crop health assessment
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spelling On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health AssessmentGálvez, Ana I.Afonso, FredericoMartínez Heredia, Juana MaríaDeep learningSmart agricultureSemantic segmentationUnmanned aerial vehiclesCrop health assessment© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).This work proposes an end-to-end solution for leaf segmentation, disease detection, and damage quantification, specifically focusing on citrus crops. The primary motivation behind this research is to enable the early detection of phytosanitary problems, which directly impact the productivity and profitability of Spanish and Portuguese agricultural developments, while ensuring environmentally safe management practices. It integrates an onboard computing module for Unmanned Aerial Vehicles (UAVs) using a Raspberry Pi 4 with Global Positioning System (GPS) and camera modules, allowing the real-time geolocation of images in citrus croplands. To address the lack of public data, a comprehensive database was created and manually labelled at the pixel level to provide accurate training data for a deep learning approach. To reduce annotation effort, we developed a custom automation algorithm for pixel-wise labelling in complex natural backgrounds. A SegNet architecture with a Visual Geometry Group 16 (VGG16) backbone was trained for the semantic, pixel-wise segmentation of citrus foliage. The model was successfully integrated as a modular component within a broader system architecture and was tested with UAV-acquired images, demonstrating accurate disease detection and quantification, even under varied conditions. The developed system provides a robust tool for the efficient monitoring of citrus crops in precision agriculture.MDPIIngeniería Electrónica2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/175224https://doi.org/10.3390/rs17132253reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésRemote Sesing, 17 (13), 2253.https://www.mdpi.com/2072-4292/17/13/2253info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1752242026-06-17T12:51:07Z
dc.title.none.fl_str_mv On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health Assessment
title On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health Assessment
spellingShingle On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health Assessment
Gálvez, Ana I.
Deep learning
Smart agriculture
Semantic segmentation
Unmanned aerial vehicles
Crop health assessment
title_short On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health Assessment
title_full On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health Assessment
title_fullStr On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health Assessment
title_full_unstemmed On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health Assessment
title_sort On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health Assessment
dc.creator.none.fl_str_mv Gálvez, Ana I.
Afonso, Frederico
Martínez Heredia, Juana María
author Gálvez, Ana I.
author_facet Gálvez, Ana I.
Afonso, Frederico
Martínez Heredia, Juana María
author_role author
author2 Afonso, Frederico
Martínez Heredia, Juana María
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería Electrónica
dc.subject.none.fl_str_mv Deep learning
Smart agriculture
Semantic segmentation
Unmanned aerial vehicles
Crop health assessment
topic Deep learning
Smart agriculture
Semantic segmentation
Unmanned aerial vehicles
Crop health assessment
description © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/175224
https://doi.org/10.3390/rs17132253
url https://hdl.handle.net/11441/175224
https://doi.org/10.3390/rs17132253
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Remote Sesing, 17 (13), 2253.
https://www.mdpi.com/2072-4292/17/13/2253
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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