Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition

Abstract

In the vision system of apple-picking robots, the main challenge is to rapidly and accurately identify the apple targets with varying halation and shadows on their surfaces. To solve this problem, this study proposes a novel, multi-feature, patch-based apple image segmentation technique using the gray-centered red–green–blue (RGB) color space. The developed method presents a multi-feature selection process, which eliminates the effect of halation and shadows in apple images. By exploring all the features of the image, including halation and shadows, in the gray-centered RGB color space, the proposed algorithm, which is a generalization of K-means clustering algorithm, provides an efficient target segmentation result. The proposed method is tested on 240 apple images. It offered an average accuracy rate of 98.79%, a recall rate of 99.91%, an F1 measure of 99.35%, a false positive rate of 0.04%, and a false negative rate of 1.18%. Compared with the classical segmentation methods and conventional clustering algorithms, as well as the popular deep-learning segmentation algorithms, the proposed method can perform with high efficiency and accuracy to guide robotic harvesting.

Object Detection and Recognition Techniques Based on Digital Image Processing and Traditional Machine Learning for Fruit and Vegetable Harvesting Robots: An Overview and Review

“…Since machine learning can derive laws from sample data that can hardly be summarized by theoretical analysis, many researchers have conducted extensive and in-depth research on techniques for object detection and recognition of fruits and vegetables based on the K-means clustering algorithm [68][69][70][71][72][73][74][75], SVM algorithm [54,57,69,73,[76][77][78][79][80][81][82][83][84], KNN clustering algorithm [36,[85][86][87][88][89][90][91], AdaBoost algorithm [62,[92][93][94][95][96][97][98][99], decision tree algorithm [100][101][102][103][104][105][106][107], and Bayesian algorithm [108]…”

“…(2) Image segmentation and classifiers based on traditional machine learning, such as K-means clustering algorithm-based methods [68][69][70][71][72][73][74][75], SVM (Support Vector Machine) algorithm-based methods [54,57,69,73,[76][77][78][79][80][81][82][83][84], KNN (K Nearest Neighbor) clustering algorithm-based methods [36,[85][86][87][88][89][90][91], AdaBoost (Adaptive Boosting) algorithm-based methods [62,[92][93][94][95][96][97][98][99], decision tree algorithm-based methods [100][101][102][103][104][105][106][107], and Bayesian algorithmbased methods [108]…”

A simple and efficient deep learning-based framework for vegetable recognition

“…[11]. Concurrently, numerous traditional machine learning-based image segmentation algorithms are available, such as SVM [12], K-means clustering [13], GrabCut [8], among others. Nonetheless, the vegetable identification method exhibits areas for enhancement.…”

Establishment of an efficient and accurate thermal stability evaluation method based on machine vision and its application in PVC thermal degradation

“…In the field of machine vision, a color can be represented by a threedimensional array consisting of three numbers between 0 and 255, and an image can be represented by multiple sets of three-dimensional arrays. 27,28 To minimize errors, it is necessary to preprocess the image with mean filtering before performing numerical conversion.…”