Automating Tomato Ripeness Classification and Counting with YOLOv9

摘要

This article proposes a novel solution to the long-standing issue of ripe (or manual) tomato monitoring and counting, often relying on visual inspection, which is both time-consuming, requires a lot of labor and prone to inaccuracies. By leveraging the power of artificial intelligence (AI) and image analysis techniques, a more efficient and precise method for automating this process is introduced. This approach promises to significantly reduce labor requirements while enhancing accuracy, thus improving overall quality and productivity. In this study, we explore the application of the latest version of YOLO (You Only Look Once), specifically YOLOv9, in automating the classification of tomato ripeness levels and counting tomatoes. To assess the performance of the proposed model, the study employs standard evaluation metrics including Precision, Recall, and mAP50. These metrics provide valuable insights into the model’s ability to accurately detect and count tomatoes in real-world scenarios. The results indicate that the YOLOv9-based model achieves superior performance, as evidenced by the following evaluation metrics: Precision: 0.856, Recall: 0.832, and mAP50: 0.882. By leveraging YOLOv9 and comprehensive evaluation metrics, this research aims to provide a robust solution for automating tomato monitoring processes. Additionally, by offering the future integration of robotics, the collection phase can further optimize efficiency and enable the expansion of cultivation areas.