Precise Apple Detection and Localization in Orchards using YOLOv5 for Robotic Harvesting Systems

TL;DR

This paper presents a YOLOv5-based system for accurate apple detection and localization in orchards, enhancing robotic harvesting efficiency through a robust, dataset-driven approach that outperforms other models.

Contribution

The paper introduces a novel YOLOv5-based method for precise apple detection and localization in complex orchard environments, supported by a curated dataset and comparative performance analysis.

Findings

Achieved approximately 85% detection accuracy.

Outperformed SSD and other models in detection tasks.

Demonstrated robustness in complex orchard conditions.

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.