Optimizing YOLOv8 for Enhanced Melon Maturity Detection with Attention Mechanisms: A Case Study from Puspalebo Orchard

Enhancing fruit maturity detection is crucial in the agricultural industry to ensure product quality and reduce post-harvest losses. However, commonly used maturity detection methods still rely on human visual inspection, which is prone to errors and assessment variability. Challenges like lighting variations, complex backgrounds, and diverse environmental conditions often complicate accurate and efficient detection. This study aims to develop and evaluate an optimized YOLOv8 model with attention mechanisms to detect melon maturity. The dataset was obtained from Puspalebo Orchard in East Java, Indonesia, comprising over a thousand melon images divided into three subsets: 70% for training, 20% for validation, and 10% for testing. The YOLOv8 model was modified to support the integration of attention mechanisms to enhance focus on significant features and detection accuracy. Data augmentation techniques were applied to capture environmental condition variations, improving the model's robustness. Evaluation on the validation subset showed a precision of 0.979 for all classes, recall of 0.962, mAP@50 of 0.981, and mAP@50-95 of 0.941. The model also demonstrated high efficiency for real-time applications with a preprocessing time of 0.1ms, inference time of 0.9ms, and post-process time of 0.9ms per image. The results of this study show advantages in detection detail, adaptability, and real-time efficiency compared to other studies in the past five years. Some weaknesses were identified, such as implementation complexity and the need for a large dataset. The developed YOLOv8 model improves melon maturity detection performance, offering a more accurate, efficient, and adaptive solution for the agricultural industry.