  The advancement of agricultural robotics holds immense promise for
transforming fruit harvesting practices, particularly within the apple
industry. The accurate detection and localization of fruits are pivotal for the
successful implementation of robotic harvesting systems. In this paper, we
propose a novel approach to apple detection and position estimation utilizing
an object detection model, YOLOv5. Our primary objective is to develop a robust
system capable of identifying apples in complex orchard environments and
providing precise location information. To achieve this, we curated an
autonomously labeled dataset comprising diverse apple tree images, which was
utilized for both training and evaluation purposes. Through rigorous
experimentation, we compared the performance of our YOLOv5-based system with
other popular object detection models, including SSD. Our results demonstrate
that the YOLOv5 model outperforms its counterparts, achieving an impressive
apple detection accuracy of approximately 85%. We believe that our proposed
system's accurate apple detection and position estimation capabilities
represent a significant advancement in agricultural robotics, laying the
groundwork for more efficient and sustainable fruit harvesting practices.
