1 Object detection, specifically fruitlet detection, is a crucial image processing technique in agricultural automation, enabling the accurate identification of fruitlets on orchard trees within images. It is vital for early fruit load management and overall crop management, facilitating the effective deployment of automation and robotics to optimize orchard productivity and resource use. This study systematically performed an extensive evaluation of the performances of all configurations of YOLOv8, YOLOv9, and YOLOv10 object detection algorithms in terms of precision, recall, mean Average Precision at 50% Intersection over Union (mAP@50), and computational speeds including pre-processing, inference, and post-processing times for fruitlet (of fruitlet) 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 investigation of total 17 different configurations of YOLOv8, YOLOv9 and YOLOv10 (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 Gelanm 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; outperformed all other configurations in YOLOv9 and YOLOv8 family of models in terms of post processing speed (only 1.5 ms), while YOLOv9 Gelan-e (1.9 ms) and YOLOv8m (2.1 ms). Furthermore, YOLOv8n exhibited the highest inference speed (detection speed) among all configurations tested, achieving a processing time of 4.1 milliseconds. In comparison, YOLOv9 Gelan-t and YOLOv10n also demonstrated impressive inference speeds of 9.3 ms and 5.5 ms, respectively Additionally, counting validation studies across four apple varieties (Scifresh, Scilate, Honeycrisp, and Cosmic Crisp) using an iPhone 14, and two varieties (Golden Delicious and Scifresh) using Microsoft Kinect Azure DK and Intel RealSense D435i sensors, highlighted YOLOv9 Gelan-e as the most accurate configuration. It achieved an RMSE of 3.11 and an MAE of 4.58, surpassing all other YOLOv8, YOLOv9, and YOLOv10 configurations in accuracy. This performance underscores the YOLOv9 models' superiority in fruitlet detection and counting within complex orchard settings. Despite some YOLOv10 configurations offering marginally better precision and recall, the YOLOv9 models provide comparable accuracy while maintaining significantly faster inference speeds, making them more suited for agricultural applications where computational resources and data availability are often constrained.