  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.
