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AI-powered solution for plant disease detection in viticulture

2024Journal article Open access
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dc.contributor.authorMadeira, Miguel
dc.contributor.authorPorfírio, Rui
dc.contributor.authorSantos, Pedro Albuquerque
dc.contributor.authorMadeira, Rui Neves
dc.date.accessioned2025-01-06T16:35:59Z
dc.date.available2025-01-06T16:35:59Z
dc.date.issued2024
dc.description.abstractIn an era dominated by the intersection of advanced technology and traditional industries, the domain of agriculture is on the verge of a revolutionary transformation. This article introduces a solution for vineyard producers, harnessing satellite imagery, weather data, and deep learning (DL) to identify vineyard diseases robustly. This solution, designed for proactive plant health management, stands as a transformative tool towards digital viticulture. Such tools transition from luxuries to essentials as vineyards confront evolving challenges like climate change and new pathogens. Our research builds on the hypothesis that customising deep learning architectures for specific tasks is crucial in enhancing their effectiveness. We contribute by introducing a tailored convolutional neural network (CNN) architecture, developed specifically for the classification of plant diseases using vineyard imagery. The experimental results demonstrate that our custom CNN architecture exhibits performance on par with established state-of-the-art models like ResNet50 and MobileNetV2, underscoring the value of specialized solutions in addressing the unique challenges of viticulture. This paper introduces an overview of the solution’s architecture, presents the implementation of DL modules with their corresponding results, and describes use case scenarios.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMadeira, M., Porfírio,R.P., Santos, P. A. & Madeira, R. N.(2024). AI-powered solution for plant disease detection in viticulture. Procedia Computer Science, 238, 468-475pt_PT
dc.identifier.doihttps://doi.org/10.1016/j.procs.2024.06.049pt_PT
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/10400.26/53497
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1877050924012857pt_PT
dc.subjectPlant Disease Detectionpt_PT
dc.subjectViticulturept_PT
dc.subjectDigital Agriculturept_PT
dc.subjectData Visualizationpt_PT
dc.subjectDeep Learningpt_PT
dc.subjectConvolutional Neural Networkspt_PT
dc.titleAI-powered solution for plant disease detection in viticulturept_PT
dc.typejournal article
dspace.entity.typePublication
person.familyNameda Costa Porfírio
person.familyNameNeves Madeira
person.givenNameRui Pedro
person.givenNameRui
person.identifier.ciencia-idF81B-016E-136E
person.identifier.ciencia-id491E-FAE5-96B3
person.identifier.orcid0009-0004-3164-4260
person.identifier.orcid0000-0001-7360-3855
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication3baea162-d1e1-49c2-a2dd-6d13f82d3022
relation.isAuthorOfPublication4e1f9ece-888f-42e0-b1ba-c6e833ae45ed
relation.isAuthorOfPublication.latestForDiscovery3baea162-d1e1-49c2-a2dd-6d13f82d3022

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