4.7 Article

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

Journal

AGRICULTURE-BASEL
Volume 11, Issue 3, Pages -

Publisher

MDPI
DOI: 10.3390/agriculture11030273

Keywords

fruit segmentation; color space; segmentation algorithm

Categories

Funding

  1. science and technology projects in Shaanxi Province Development and Application of key equipment for Orchard Mechanization and Intelligence [2020zdzx03-04-01]
  2. National Natural Science Foundation of China [61971005]

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This study proposes a novel method for apple image segmentation using multiple feature selection and gray-centered RGB color space, which can efficiently and accurately identify apple targets. In experiments, the proposed method demonstrated very high accuracy and recall rates.
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.

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