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

Pan Fan, Guodong Lang, Pengju Guo, Zhijie Liu, Fuzeng Yang, Bin Yan and Xiaoyan Lei
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Pan Fan: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Guodong Lang: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Pengju Guo: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Zhijie Liu: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Fuzeng Yang: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Bin Yan: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Xiaoyan Lei: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China

Agriculture, 2021, vol. 11, issue 3, 1-18

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.

Keywords: fruit segmentation; color space; segmentation algorithm (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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