Comprehensive Performance Evaluation of YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments

원문 초록

This study extensively evaluated You Only Look Once (YOLO) object detection algorithms across all configurations (total 22) of YOLOv8, YOLOv9, YOLOv10, and YOLO11 (or YOLOv11) for green fruit detection in commercial orchards. The research also validated in-field fruitlet counting using an iPhone and machine vision sensors across four apple varieties: Scifresh, Scilate, Honeycrisp and Cosmic Crisp. Among the 22 configurations evaluated, YOLOv11s and YOLOv9 gelan-base outperformed others with mAP@50 scores of 0.933 and 0.935 respectively. In terms of recall, YOLOv9 gelan-base achieved the highest value among YOLOv9 configurations at 0.899, while YOLOv11m led YOLOv11 variants with 0.897. YOLO11n emerged as the fastest model, achieving fastest inference speed of only 2.4 ms, significantly outpacing the leading configurations of YOLOv10n, YOLOv9 gelan-s, and YOLOv8n, with speeds of 5.5, 11.5, and 4.1 ms, respectively. This comparative evaluation highlights the strengths of YOLOv11, YOLOv9, and YOLOv10, offering researchers essential insights to choose the best-suited model for fruitlet detection and possible automation in commercial orchards. For real-time automation related work in relevant datasets, we recommend using YOLOv11n due to its high detection and image processing speed. Keywords: YOLO11, YOLO11 Object Detection, YOLOv10, YOLOv9, YOLOv8, You Only Look Once, Fruitlet Detection, Greenfruit Detection, YOLOv11 bounding box, YOLOv11 detection, YOLOv11 object detection, YOLOv11 machine learning, YOLOv11 Deep Learning