Deep learning-based real-time detection and classification of tomato ripeness stages using YOLOv8 on raspberry Pi

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Abstract The automated detection of tomato ripeness is critical in crop management and harvesting. In most earlier works, tomato image ripeness detection has been based upon a limited set of images and binary classification (ripe and unripe). This study uses the cutting-edge YOLOv8 object detection algorithm and a comprehensive dataset to propose an accurate real-time system for detecting and classifying tomato ripeness (multi-class). Based on two open-source datasets (Kaggle and Internet-sourced), we developed and tested the proposed system. In this method, a comprehensive tomato image dataset is curated, YOLOv8 models are built, seamlessly integrated into an embedded system (Raspberry Pi4), then evaluated and validated. The model shows exceptional performance in detecting three distinct classes of ripeness: green, partially ripe, and ripe. It surpasses existing state-of-the-art models in both accuracy and efficiency. Based on the Kaggle dataset, our model achieves an average precision at 50 of 0.808, with F1-scores of 0.80, 0.65, and 0.796 for green, partially ripe, and ripe classes, respectively. It achieves mAP at 50 of 0.725 and F1-scores of 0.747 (green), 0.652 (partially ripe), and 0.72 (ripe) for the corresponding classes of the Internet-sourced Dataset, exceeding current state-of-the-art models. Finally, the proposed tomato ripeness detection algorithm is implemented on the Raspberry Pi4 system and exhibits notable performance. With the integration of YOLOv8 into an embedded system (Respbeery Pi4), it can be used to improve efficiency and reduce labor costs in tomato-picking robots, helping to revolutionize agricultural practices.