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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.
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    Tomato Disease Detection to Reduce Food Waste
    (SPIE, 2025) Ghizawi, Obadah; Sallam, Hamza; Sonmez, Elena Battini
    The 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.