Comparative Study of Object Detection Models for Fresh and Rotten Apples and Tomatoes: Faster R-CNN, DETR, YOLOv8, and YOLOv12S

The scientific and technological principles applied in agrotechnology improve the effectiveness, productivity, and sustainability of the agricultural system. Four of the high-performance object detection models, namely You Only Look Once version 8 (YOLOv8), YOLOv12S, Detection Transformers (DETR), and Faster Region-based Convolutional Neural Network (R-CNN), were compared using key performance metrics including mean Average Precision (mAP) and F1-Score to classify rotten and fresh tomatoes and apples. YOLOv12S stands out among the four models on a custom dataset of size 11,675 with a mean Average Precision (mAP) score of 99.31% and F1-Score of 98.28%. YOLOv12S is well-suited for real-time applications as it optimizes the trade-off between computational cost and accuracy. The architectural analysis highlights YOLOv12S's CNN backbone for fast inference versus DETR's transformer-based global context modeling. Unlike past efforts focusing on single models, the comparison spans architectures, revealing their strengths, weaknesses, and practical roles in farming. Future work should address computational demands, hybrid models, and adaptability.