This study extensively evaluated You Only Look Once (YOLO) object detection algorithms across all configurations (total 22) of YOLOv8, YOLOv9, YOLOv10, and YOLO11 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, YOLO11s 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 YOLO11m led YOLO11 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 YOLO11, 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 YOLO11n 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, Green Apple Detection, Agricultural Automation, Artificial Intelligence, Deep Learning, Machine Learning, Zero-shot Detection

翻译：本研究在商业果园环境中，对YOLOv8、YOLOv9、YOLOv10及YOLO11的所有配置（共计22种）进行了全面的“You Only Look Once”（YOLO）目标检测算法评估，旨在实现绿色果实的检测。研究还利用iPhone和机器视觉传感器，在四个苹果品种（Scifresh、Scilate、Honeycrisp和Cosmic Crisp）上验证了田间幼果计数。在所评估的22种配置中，YOLO11s和YOLOv9 gelan-base表现最佳，其mAP@50分数分别为0.933和0.935。在召回率方面，YOLOv9 gelan-base在YOLOv9配置中以0.899的成绩位居榜首，而YOLO11m则在YOLO11变体中以0.897领先。YOLO11n成为速度最快的模型，其最快推理速度仅为2.4毫秒，显著超越了YOLOv10n、YOLOv9 gelan-s和YOLOv8n等领先配置（速度分别为5.5、11.5和4.1毫秒）。此项对比评估凸显了YOLO11、YOLOv9和YOLOv10的优势，为研究人员选择最适合商业果园幼果检测及潜在自动化应用的模型提供了重要参考。针对相关数据集的实时自动化相关工作，我们推荐使用YOLO11n，因其具备较高的检测与图像处理速度。关键词：YOLO11, YOLO11目标检测, YOLOv10, YOLOv9, YOLOv8, You Only Look Once, 幼果检测, 绿色果实检测, 青苹果检测, 农业自动化, 人工智能, 深度学习, 机器学习, 零样本检测