Tomato Disease Detection to Reduce Food Waste

Basit Öğe Kaydı
dc.contributor.authorGhizawi, Obadah
dc.contributor.authorSallam, Hamza
dc.contributor.authorSonmez, Elena Battini
dc.date.accessioned2026-04-04T18:48:33Z
dc.date.available2026-04-04T18:48:33Z
dc.date.issued2025
dc.description17th International Conference on Machine Vision, ICMV 2024 -- 10 October 2024 through 13 October 2024 -- Edinburg -- 207255
dc.description.abstractThe issue of detecting diseases in tomato leaves plays a major role in agricultural sustainability and food safety. Tomatoes are the second most important fruit crop after potatoes. Currently, almost 90% of edible tomatoes are thrown away, significantly contributing to overall fruit and vegetable waste. This research compares the performance of several Convolutional Neural Network (CNN) with the aim to efficiently detect diseases in tomatoes and reduce food waste. From VGG-16 to EfficientNet-B0, six different architectures have been tested to classify 10 distinct diseases of tomatoes using a subset of the public PlantVillage dataset. The results concluded that the best-performing model is the EfficientNet-B0 architecture, with a 96.04% test accuracy. Future work includes augmenting the dataset with more images using C-GAN and alternative techniques, as well as testing different tricks to improve the current performance. The final aim is to contribute to the design of a successful and robust artificial intelligence tool capable of reducing loss and waste of tomatoes. © 2025 SPIE.
dc.description.sponsorshipRussian Academy of Sciences; University of Stuttgart
dc.identifier.doi10.1117/12.3055037
dc.identifier.isbn978-151068827-8
dc.identifier.issn0277-786X
dc.identifier.scopus2-s2.0-105000204781
dc.identifier.scopusqualityQ4
dc.identifier.urihttps://doi.org/10.1117/12.3055037
dc.identifier.urihttps://hdl.handle.net/11411/10227
dc.identifier.volume13517
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSPIE
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineering
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260402
dc.subjectCnn
dc.subjectDeep Learning
dc.subjectFood Waste
dc.subjectFruits And Vegetables Loss And Wastes
dc.subjectImage Classification
dc.subjectTomato Diseases
dc.titleTomato Disease Detection to Reduce Food Waste
dc.typeConference Paper

Dosyalar

Koleksiyon

Scopus Indexed Publications