Cluster segmentation and stereo vision-based apple localization algorithm for robotic harvesting.

ABSTRACT:

Introduction

Automated apple harvesting is hindered by clustered fruits, varying illumination, and inconsistent depth perception in complex orchard environments. While deep learning models such as Faster R-CNN and YOLO provide accurate 2D detection, they require large annotated datasets and high computational resources, and often lack the precise 3D localisation required for robotic picking.

Methods

This study proposes an enhanced K-Means clustering segmentation algorithm integrated with a stereo-vision system for accurate 3D apple localisation. Multi-feature fusion combining colour, morphology, and texture descriptors was applied to improve segmentation robustness. A block-matching stereo model was used to compute disparity and derive 3D coordinates. The method was evaluate

SUBMITTER: Wang J 

PROVIDER: S-EPMC12695797 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature