Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps

Food security is a pressing global concern, particularly highlighted by the United Nations Sustainable Development Goal 2 (SDG 2), which focuses on enhancing the productivity and incomes of smallholder farmers. In the Middle East and North Africa (MENA) region, horticultural crops are increasingly t...

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Autores:
Buchaillot, Ma. Luisa
Fernandez-Gallego, Jose A
Mahmoudi, Henda
Thushar, Sumitha
Aljanaahi, Amna Abdulnoor
Kosimov, Sherzod
Hammami, Zied
Al Jabri, Ghazi
Puente, Alexandra La Cruz
Akl, Alexi
Trillas, M. Isabel
Araus, Jose Luis
Kefauver, Shawn C.
Tipo de recurso:
Article of investigation
Fecha de publicación:
2024
Institución:
Universidad de Ibagué
Repositorio:
Repositorio Universidad de Ibagué
Idioma:
eng
OAI Identifier:
oai:repositorio.unibague.edu.co:20.500.12313/6056
Acceso en línea:
https://hdl.handle.net/20.500.12313/6056
https://sciencedirect.unibague.elogim.com/science/article/pii/S1574954124004424
Palabra clave:
Cultivos hortículas - Trastornos
Data collection
Deep learning
Horticultural crops
MENA
Mobile app
SDG 17
SDG 2
Rights
openAccess
License
© 2024 The Authors
id UNIBAGUE2_6c1cacdf63b18296364f6fd7916796dc
oai_identifier_str oai:repositorio.unibague.edu.co:20.500.12313/6056
network_acronym_str UNIBAGUE2
network_name_str Repositorio Universidad de Ibagué
repository_id_str
dc.title.eng.fl_str_mv Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps
title Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps
spellingShingle Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps
Cultivos hortículas - Trastornos
Data collection
Deep learning
Horticultural crops
MENA
Mobile app
SDG 17
SDG 2
title_short Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps
title_full Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps
title_fullStr Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps
title_full_unstemmed Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps
title_sort Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps
dc.creator.fl_str_mv Buchaillot, Ma. Luisa
Fernandez-Gallego, Jose A
Mahmoudi, Henda
Thushar, Sumitha
Aljanaahi, Amna Abdulnoor
Kosimov, Sherzod
Hammami, Zied
Al Jabri, Ghazi
Puente, Alexandra La Cruz
Akl, Alexi
Trillas, M. Isabel
Araus, Jose Luis
Kefauver, Shawn C.
dc.contributor.author.none.fl_str_mv Buchaillot, Ma. Luisa
Fernandez-Gallego, Jose A
Mahmoudi, Henda
Thushar, Sumitha
Aljanaahi, Amna Abdulnoor
Kosimov, Sherzod
Hammami, Zied
Al Jabri, Ghazi
Puente, Alexandra La Cruz
Akl, Alexi
Trillas, M. Isabel
Araus, Jose Luis
Kefauver, Shawn C.
dc.subject.armarc.none.fl_str_mv Cultivos hortículas - Trastornos
topic Cultivos hortículas - Trastornos
Data collection
Deep learning
Horticultural crops
MENA
Mobile app
SDG 17
SDG 2
dc.subject.proposal.eng.fl_str_mv Data collection
Deep learning
Horticultural crops
MENA
Mobile app
SDG 17
SDG 2
description Food security is a pressing global concern, particularly highlighted by the United Nations Sustainable Development Goal 2 (SDG 2), which focuses on enhancing the productivity and incomes of smallholder farmers. In the Middle East and North Africa (MENA) region, horticultural crops are increasingly threatened by pests and diseases, exacerbated by climate change. Local farmers often lack the necessary expertise to effectively manage these issues, resulting in significant reductions in both yield and quality of their crops. This study seeks to develop an accessible mobile crop diagnosis application. By utilizing machine learning and deep learning technologies, the app is designed to help MENA farmers quickly and accurately identify and treat crop disorders. We used Open Data Kit (ODK) to gather a large dataset of crop images required to train deep learning models. These models, built on open-source deep learning architectures, were designed to classify 21 different leaf disorders, including diseases, pests, and nutritional deficiencies. The system was implemented in both a web app and an Android mobile app. Our deep learning models demonstrated an overall accuracy of 94 % in diagnosing plant disorders. The app, Doctor Nabat, includes a decision support system that offers treatment options in the three primary languages spoken in the MENA region. Doctor Nabat is an effective and scalable tool for enhancing crop management in the MENA region, promoting food security by minimizing crop losses through improved pest and disease diagnosis and treatment strategies.
publishDate 2024
dc.date.issued.none.fl_str_mv 2024-12
dc.date.accessioned.none.fl_str_mv 2025-11-28T21:59:02Z
dc.date.available.none.fl_str_mv 2025-11-28T21:59:02Z
dc.type.none.fl_str_mv Artículo de revista
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.none.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.content.none.fl_str_mv Text
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.citation.none.fl_str_mv Buchaillot, M., Fernandez-Gallego, J., Mahmoudi, H., Thushar, S., Aljanaahi, A., Kosimov, S., Hammami, Z., Al Jabri, G., Puente, A., Akl, A ., Trillas, M., Araus, J. Kefauver, S. (2024). Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps. Ecological Informatics, 84. DOI: 10.1016/j.ecoinf.2024.102900.
dc.identifier.doi.none.fl_str_mv 10.1016/j.ecoinf.2024.102900
dc.identifier.issn.none.fl_str_mv 15749541
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12313/6056
dc.identifier.url.none.fl_str_mv https://sciencedirect.unibague.elogim.com/science/article/pii/S1574954124004424
identifier_str_mv Buchaillot, M., Fernandez-Gallego, J., Mahmoudi, H., Thushar, S., Aljanaahi, A., Kosimov, S., Hammami, Z., Al Jabri, G., Puente, A., Akl, A ., Trillas, M., Araus, J. Kefauver, S. (2024). Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps. Ecological Informatics, 84. DOI: 10.1016/j.ecoinf.2024.102900.
10.1016/j.ecoinf.2024.102900
15749541
url https://hdl.handle.net/20.500.12313/6056
https://sciencedirect.unibague.elogim.com/science/article/pii/S1574954124004424
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.citationvolume.none.fl_str_mv 84
dc.relation.ispartofjournal.none.fl_str_mv Ecological Informatics
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spelling Buchaillot, Ma. Luisaa3d6aadf-1d90-409f-8675-733309261850-1Fernandez-Gallego, Jose A607b6baa-5d34-493a-abac-60953121c170-1Mahmoudi, Hendaee151e14-de42-4f6c-8ecf-de758d0e9e5d-1Thushar, Sumithaf660a66f-8bf4-4280-9831-0b115fb0d217-1Aljanaahi, Amna Abdulnoor0f19e183-074c-47e0-9642-67729f7dee4d-1Kosimov, Sherzoddcca7ef1-cc2a-4b49-92de-ed2222536862-1Hammami, Zied1cb98018-0728-4f98-887c-0c04cb2f0231-1Al Jabri, Ghazi4e24e1f6-a263-4ea4-b1e1-58368769b0b2-1Puente, Alexandra La Cruz4086fc38-14c2-4958-971e-7330bf0b747a-1Akl, Alexi7fbc9f8b-2976-4889-86af-512feba19d0e-1Trillas, M. Isabel88a6e1c3-d995-43e4-a84d-a44b343c66ef-1Araus, Jose Luis13489375-7128-4b65-8eb7-7a949a30f5d8-1Kefauver, Shawn C.76890592-b3e0-4e2d-8c37-f79eadf96e68-12025-11-28T21:59:02Z2025-11-28T21:59:02Z2024-12Food security is a pressing global concern, particularly highlighted by the United Nations Sustainable Development Goal 2 (SDG 2), which focuses on enhancing the productivity and incomes of smallholder farmers. In the Middle East and North Africa (MENA) region, horticultural crops are increasingly threatened by pests and diseases, exacerbated by climate change. Local farmers often lack the necessary expertise to effectively manage these issues, resulting in significant reductions in both yield and quality of their crops. This study seeks to develop an accessible mobile crop diagnosis application. By utilizing machine learning and deep learning technologies, the app is designed to help MENA farmers quickly and accurately identify and treat crop disorders. We used Open Data Kit (ODK) to gather a large dataset of crop images required to train deep learning models. These models, built on open-source deep learning architectures, were designed to classify 21 different leaf disorders, including diseases, pests, and nutritional deficiencies. The system was implemented in both a web app and an Android mobile app. Our deep learning models demonstrated an overall accuracy of 94 % in diagnosing plant disorders. The app, Doctor Nabat, includes a decision support system that offers treatment options in the three primary languages spoken in the MENA region. Doctor Nabat is an effective and scalable tool for enhancing crop management in the MENA region, promoting food security by minimizing crop losses through improved pest and disease diagnosis and treatment strategies.application/pdfBuchaillot, M., Fernandez-Gallego, J., Mahmoudi, H., Thushar, S., Aljanaahi, A., Kosimov, S., Hammami, Z., Al Jabri, G., Puente, A., Akl, A ., Trillas, M., Araus, J. Kefauver, S. (2024). Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps. Ecological Informatics, 84. DOI: 10.1016/j.ecoinf.2024.102900.10.1016/j.ecoinf.2024.10290015749541https://hdl.handle.net/20.500.12313/6056https://sciencedirect.unibague.elogim.com/science/article/pii/S1574954124004424engElsevier B.V.Países bajos84Ecological InformaticsAqeel-Ur-Rehman, Abbasi A.Z., Islam, N., Shaikh, Z.A., 2014. A review of wireless sensors and networks’ applications in agriculture. Comput. Stand. Interf. 36, 263–270. https://doi.org/10.1016/j.csi.2011.03.004.Arivazhagan, S., Shebiah, R.N., Ananthi, S., Vishnu Varthini, S., 2013. Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric. Eng. Int. CIGR J. 15, 211–217. AArnal Barbedo, J.G., 2019. Plant disease identification from individual lesions and spots using deep learning. Biosyst. 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Wireless sensors in agriculture and food industry - Recent development and future perspective. Comput. Electron. Agric. 50, 1–14. https://doi.org/10.1016/j.compag.2005.09.003.Yuan, L., Huang, Y., Loraamm, R.W., Nie, C., Wang, J., Zhang, J., 2014. Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects. F. Crop. Res. 156, 199–207. https://doi.org/10.1016/j.fcr.2013.11.012.Zeiler, M.D., Fergus, R., 2014. Visualizing and understanding convolutional networks. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 8689 LNCS, pp. 818–833. https://doi.org/10.1007/978-3-319- 10590-1_53.© 2024 The Authorsinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/Cultivos hortículas - TrastornosData collectionDeep learningHorticultural cropsMENAMobile appSDG 17SDG 2Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile appsArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/29a2ddc0-4a15-4cae-b1b0-cf5126ee3822/download2fa3e590786b9c0f3ceba1b9656b7ac3MD51ORIGINALArtículo.pdfArtículo.pdfapplication/pdf143919https://repositorio.unibague.edu.co/bitstreams/8185d85e-b647-4009-99fe-4813ef3c2b5f/downloada17d3386a0f645492b63acef98c5ce2fMD52TEXTArtículo.pdf.txtArtículo.pdf.txtExtracted texttext/plain3185https://repositorio.unibague.edu.co/bitstreams/5822a43f-56da-45ed-bfa7-f64532ed701f/downloadb64b27164ae6bae8b3c2eb9c4db177cfMD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg23493https://repositorio.unibague.edu.co/bitstreams/a0bf3866-1ae2-47e2-b728-cc1981846c08/downloade61fdf5b016632f4ed15d9150a25f377MD5420.500.12313/6056oai:repositorio.unibague.edu.co:20.500.12313/60562025-11-29 03:02:09.2https://creativecommons.org/licenses/by/4.0/© 2024 The Authorshttps://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8=

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