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    A computer vision approach for classifying the degree of freshness in fruits and vegetables to reduce food waste
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sallam, Hamza; Ghizawi, Obadah Nidal; Derrija, Abdulmoez S.; Sönmez, Elena Battini
    The 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.