A computer vision approach for classifying the degree of freshness in fruits and vegetables to reduce food waste

Sallam, HamzaGhizawi, Obadah NidalDerrija, Abdulmoez S.Sönmez, Elena Battini2026-04-042026-04-042024979-835037943-3https://doi.org/10.1109/ASYU62119.2024.10757112https://hdl.handle.net/11411/102292024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562The level of freshness of fruits and vegetables must be constantly monitored to reduce food waste and maximize its nutritional value. Existing approaches to this problem either consider a binary classification problem, where fruits and vegetables are classified into the two classes of”fresh” or”rotten”, or a three-class problem, with”fresh”, ”medium”, and”rotten” classes. This work challenges the three-class issue achieving an initial accuracy of 82%, using the VGG16 deep learning model, and 84%, with YOLO v5. After going through many experiments, we reached 94.58% test accuracy with VGG16, improving the current state-of-the-art performance by 11 percentage points. A detailed description of the experiments and the used algorithms, together with their hyper-parameters, is given in this paper to facilitate code reproduction. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessDeep LearningDegree Of FreshnessFood WasteFruits And Vegetables ClassificationVgg16A computer vision approach for classifying the degree of freshness in fruits and vegetables to reduce food wasteConference Paper2-s2.0-8521331360310.1109/ASYU62119.2024.10757112N/A