Apple-Harvesting Robot Based on the YOLOv5-RACF Model

Abstract

To address the issue of automated apple harvesting in orchards, we propose a YOLOv5-RACF algorithm for identifying apples and calculating apple diameters. This algorithm employs the robot operating dystem (ROS) to control the robot’s locomotion system, Lidar mapping, and navigation, as well as the robotic arm’s posture and grasping operations, achieving automated apple harvesting and placement. The tests were conducted in an actual orchard environment. The algorithm model achieved an average apple detection accuracy ([email protected]) of 98.748% and a ([email protected]:0.95) of 90.02%. The time to calculate the diameter of one apple was 0.13 s, with a measurement accuracy within an error range of 1–3 mm. The robot takes an average of 9 s to pick an apple and return to the initial pose. These results demonstrate the system’s efficiency and reliability in real agricultural environments.

Keywords

• YOLOv5
• apple harvesting
• autonomous navigation
• object detection
• image processing