An apple recognition method based on color space

With the development of agricultural intelligence, robots replacing manual labor in agricultural production has become an inevitable trend. For apple picking robots, accurate identification of apples is the primary step in their work. At present, the existing L*a*b and OHTA color spaces have problems of incomplete extraction of apple regions and incomplete differentiation of fruit and leaves. To address this issue, this study designed a color space (LPX color space) specifically for apple recognition, and based on this, proposed a complete algorithm for identifying green or red apples in a complex open environment. The experimental results show that the algorithm has high recognition accuracy in both cases of apples being shaded and unshaded, and can provide accurate orientation for the subsequent automated apple picking.

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Publication Details

Published
2024-08-23
DOI
https://doi.org/10.1117/12.3038462
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

An apple recognition method based on color space

Xia Dong, Haibo Xu, Gengyang Song, Huatao Song
Smart Agriculture and AI
article

An apple recognition method based on color space

Xia Dong, Haibo Xu, Gengyang Song, Huatao Song
article en

Abstract

With the development of agricultural intelligence, robots replacing manual labor in agricultural production has become an inevitable trend. For apple picking robots, accurate identification of apples is the primary step in their work. At present, the existing L*a*b and OHTA color spaces have problems of incomplete extraction of apple regions and incomplete differentiation of fruit and leaves. To address this issue, this study designed a color space (LPX color space) specifically for apple recognition, and based on this, proposed a complete algorithm for identifying green or red apples in a complex open environment. The experimental results show that the algorithm has high recognition accuracy in both cases of apples being shaded and unshaded, and can provide accurate orientation for the subsequent automated apple picking.

Xi'an Jiaotong University (CN)
Openalex Percentile: Top 93%
Smart Agriculture and AI
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