Perfect pick: A comparative CNN framework for visual quality assessment of fruits and vegetables
Consumer demand for high-quality fresh fruits and vegetables continues to increase. The traditional methods applied for the selection of fruits and vegetables rely on subjective visual assessment, which has proven inadequate, leading to unnecessary food waste and inconsistencies. An artificial intelligence-based image-classification framework is presented to assist with visual assessment of produce quality. Beyond consumer usage, the trained models are well-suited for integration into robotic and automated systems that can be used in greenhouses, supermarkets, fresh markets, and distribution warehouses, where the automated classification of products by healthiness and freshness is necessary. Deep learning models—VGG- 16, VGG-19, ResNet-50, and MobileNet—were compared in the current work to determine the best-suited model to handle this classification task. The dataset comprised 10 categories: five fruits—pomegranate, orange, apple, papaya, and banana—and five vegetables—tomato, radish, capsicum, potato, and cauliflower. After hyperparameter tuning, ResNet-50 achieved the highest classification accuracy of 96.1%, outperforming the other models.
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