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An apple image segmentation method based on a color index obtained by a genetic algorithm

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Abstract

Harvesting and spraying robots based on machine vision have become important in modern agricultural engineering. Fast segmentation of fruit images is the main task of robotic picking and spraying in orchards. In this paper, a color index-based segmentation method for apple images was proposed. It can automatically obtain a specific color index according to the image segmentation task. This method was used to obtain color index and segmentation thresholds for segmenting apple images. The mean pixel segment accuracy, mean intersection over union, mean dice similarity coefficient, and mean segment time was 0.90, 0.81, 0.98, and 20 (ms). The result showed that this method could segment apple in orchard images effectively and fastly. It could be used in precise apple planting management. At the same time, this paper provided a systematic method to get a specific color index.

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Acknowledgements

This research was funded by the National Key Research and Development Project (2019YFB1312303) and the National Natural Science Foundation of China(31601217). The authors thank Yue Pan, Zhang Fan, Han Wang, and Qianfeng Liao for their help in revising this article.

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Authors and Affiliations

  1. College of Engineering, China Agricultural University, Qinghua Rd.(E) No.17, Haidian District, Beijing, 100083, People’s Republic of China

    Kunlin Zou, Luzhen Ge, Hang Zhou, Chunlong Zhang & Wei Li

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  1. Kunlin Zou
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  2. Luzhen Ge
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  3. Hang Zhou
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  4. Chunlong Zhang
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  5. Wei Li
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Correspondence to Chunlong Zhang.

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Zou, K., Ge, L., Zhou, H. et al. An apple image segmentation method based on a color index obtained by a genetic algorithm. Multimed Tools Appl 81, 8139–8153 (2022). https://doi.org/10.1007/s11042-022-11905-4

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