Automatic Detection, Positioning and Counting of Grape Bunches Using Robots

TL;DR

This paper presents an integrated system using deep learning and spatial algorithms to automatically detect, locate, and count grape bunches with agricultural robots, enhancing precision in vineyard management.

Contribution

It introduces a novel combination of Yolov3 detection, local tracking, and 3D spatial positioning for grape bunch counting in robotic vineyard applications.

Findings

High accuracy detection of grape bunches achieved

Effective 3D positioning of grape bunches demonstrated

Successful deployment on agricultural robots in simulated environments

Abstract

In order to promote agricultural automatic picking and yield estimation technology, this project designs a set of automatic detection, positioning and counting algorithms for grape bunches, and applies it to agricultural robots. The Yolov3 detection network is used to realize the accurate detection of grape bunches, and the local tracking algorithm is added to eliminate relocation. Then it obtains the accurate 3D spatial position of the central points of grape bunches using the depth distance and the spatial restriction method. Finally, the counting of grape bunches is completed. It is verified using the agricultural robot in the simulated vineyard environment. The project code is released at: https://github.com/XuminGaoGithub/Grape_bunches_count_using_robots.