Optimized YOLO architectures for efficient Kiwi detection in precision agriculture on embedded systems.

Accurate and efficient fruit detection is essential for precision agriculture, particularly in densely occluded crops such as kiwifruit. This study presents a comprehensive benchmarking and optimization framework covering YOLOv8–YOLOv11 architectures for kiwi detection, evaluated under both high-performance training conditions and embedded deployment on an NVIDIA Jetson TX2. A field-collected dataset containing 2,925 training and 1,936 test annotations was used to train five sub-models (n, s, m, l, x) per YOLO version under identical settings. To enhance efficiency for edge deployment, a structured hyperparameter optimization procedure was applied to all “s” models, yielding substantial performance gains without additional architectural modifications. Among all evaluated models, the optimized YOLOv11s achieved the best accuracy–efficiency trade-off, reaching mAP@0.5 = 0.956, precision = 0.868, recall = 0.918, and an embedded inference time of 3.33 s/image on Jetson TX2. While larger models (e.g., YOLOv8x, YOLOv11l) attained slightly higher raw accuracies (up to mAP@0.5 = 0.957), their latency rendered them unsuitable for edge deployment. The results demonstrate that lightweight YOLO architectures, when supported by targeted hyperparameter tuning, can be effectively adapted for resource-constrained agricultural systems. The proposed evaluation and optimization pipeline provides a transferable methodology for other fruit-detection tasks and supports future development of embedded vision solutions in precision agriculture.