Precise Apple Detection and Localization in Orchards using YOLOv5 for Robotic Harvesting Systems
Jiang Ziyue, Yin Bo, Lu Boyun
TL;DR
The paper addresses robust apple detection and localization in orchard environments for robotic harvesting. It adopts a YOLOv5-based approach trained on an autonomously labeled dataset and compares performance against SSD, reporting an approximate 85% detection accuracy. The results indicate that YOLOv5 provides favorable speed and model size with competitive accuracy, supporting real-time operation in challenging lighting and occlusion conditions. This work advances agricultural robotics by enabling accurate fruit localization, contributing to more efficient and sustainable robotic harvesting workflows.
Abstract
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.