Multi-class fruit ripeness detection using YOLO and SSD object detection models

Accurate fruit ripeness detection is critical to reducing post-harvest losses and improving quality control in agricultural systems. This study benchmarks four object detection models—YOLOv5, YOLOv6, YOLOv7, and SSD-MobileNetv1—for multi-class ripeness classification of strawberries and avocados across four stages: unripe, partially ripe, ripe, and rotten. The dataset, captured under natural conditions, has been manually annotated and published for public access. YOLOv6 achieved the highest mean Average Precision (99.5%) and demonstrated a strong balance between accuracy and real-time inference speed (85.2 FPS). All models were evaluated using standard classification metrics and cross-validated through a 5-fold approach to ensure robustness. The results indicate YOLOv6 as the most reliable model for smart fruit sorting and quality monitoring applications. This study offers a reproducible benchmarking pipeline and contributes toward the development of deployable deep learning solutions in precision agriculture.