Comprehensive Performance Evaluation of YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments
Accurate detection and counting of immature and young apples in complex orchard environments remains challenging due to occlusion, varying lighting, and dense foliage. Method: This study systematically evaluates 22 model configurations spanning YOLOv8 to YOLOv11 across four apple cultivars (e.g., Scifresh, Honeycrisp), using field-collected data from both iPhone and industrial machine vision sensors. Performance is assessed via mAP@50, recall, and millisecond-level inference latency. Contribution/Results: We present the first multi-dimensional comparison of state-of-the-art models—including YOLOv11 (s/m/n) and YOLOv9 Gelan-series—under real-world orchard conditions, and propose a “lightweightness–accuracy–speed” co-design principle for agricultural automation. Results show YOLOv11s and YOLOv9 Gelan-base achieve top-tier mAP@50 of 0.933 and 0.935, respectively; YOLOv11n attains ultra-low latency of 2.4 ms—over 40% faster than YOLOv8n—demonstrating feasibility of edge-deployable, real-time fruit counting.