This study performed an extensive evaluation of the performances of all configurations of YOLOv8, YOLOv9, and YOLOv10 object detection algorithms for fruitlet (of green fruit) detection in commercial orchards. Additionally, this research performed and validated in-field counting of fruitlets using an iPhone and machine vision sensors in 5 different apple varieties (Scifresh, Scilate, Honeycrisp, Cosmic crisp & Golden delicious). This comprehensive investigation of total 17 different configurations (5 for YOLOv8, 6 for YOLOv9 and 6 for YOLOv10) revealed that YOLOv9 outperforms YOLOv10 and YOLOv8 in terms of mAP@50, while YOLOv10x outperformed all 17 configurations tested in terms of precision and recall. Specifically, YOLOv9 Gelan-e achieved the highest mAP@50 of 0.935, outperforming YOLOv10n's 0.921 and YOLOv8s's 0.924. In terms of precision, YOLOv10x achieved the highest precision of 0.908, indicating superior object identification accuracy compared to other configurations tested (e.g. YOLOv9 Gelan-c with a precision of 0.903 and YOLOv8m with 0.897. In terms of recall, YOLOv10s achieved the highest in its series (0.872), while YOLOv9 Gelan m performed the best among YOLOv9 configurations (0.899), and YOLOv8n performed the best among the YOLOv8 configurations (0.883). Meanwhile, three configurations of YOLOv10: YOLOv10b, YOLOv10l, and YOLOv10x achieved superior post-processing speeds of 1.5 milliseconds, outperforming all other configurations within the YOLOv9 and YOLOv8 families. Specifically, YOLOv9 Gelan-e recorded a post-processing speed of 1.9 milliseconds, and YOLOv8m achieved 2.1 milliseconds. Furthermore, YOLOv8n exhibited the highest inference speed among all configurations tested, achieving a processing time of 4.1 milliseconds while YOLOv9 Gelan-t and YOLOv10n also demonstrated comparatively slower inference speeds of 9.3 ms and 5.5 ms, respectively.

翻译：本研究对YOLOv8、YOLOv9和YOLOv10目标检测算法所有配置版本在商业化果园中幼果（绿色果实）检测的性能进行了全面评估。此外，本研究利用iPhone与机器视觉传感器对5个不同苹果品种（Scifresh、Scilate、Honeycrisp、Cosmic crisp和Golden delicious）进行了田间幼果计数验证。通过对总计17种不同配置（YOLOv8五种、YOLOv9六种、YOLOv10六种）的系统性研究发现：在mAP@50指标上，YOLOv9整体优于YOLOv10和YOLOv8；而YOLOv10x在精确率与召回率方面均优于所有测试的17种配置。具体而言，YOLOv9 Gelan-e取得了最高的mAP@50值0.935，优于YOLOv10n的0.921和YOLOv8s的0.924。在精确率方面，YOLOv10x以0.908的精确率位居首位，表明其相较于其他测试配置（如YOLOv9 Gelan-c精确率0.903、YOLOv8m精确率0.897）具有更优的目标识别准确度。在召回率方面，YOLOv10s在其系列中表现最佳（0.872），YOLOv9 Gelan-m在YOLOv9配置中表现最优（0.899），YOLOv8n则在YOLOv8配置中取得最佳召回率（0.883）。同时，YOLOv10的三种配置（YOLOv10b、YOLOv10l和YOLOv10x）实现了1.5毫秒的卓越后处理速度，优于YOLOv9和YOLOv8系列的所有其他配置。具体而言，YOLOv9 Gelan-e的后处理速度为1.9毫秒，YOLOv8m为2.1毫秒。此外，在所有测试配置中，YOLOv8n展现出最高的推理速度，处理时间仅为4.1毫秒；而YOLOv9 Gelan-t和YOLOv10n的推理速度相对较慢，分别为9.3毫秒和5.5毫秒。