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Image Segmentation of Cucumber Seedlings Based on Genetic Algorithm

Author

Listed:
  • Taotao Xu

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China)

  • Lijian Yao

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China)

  • Lijun Xu

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China)

  • Qinhan Chen

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China)

  • Zidong Yang

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China)

Abstract

To solve the problems of the low target-positioning accuracy and weak algorithm robustness of target-dosing robots in greenhouse environments, an image segmentation method for cucumber seedlings based on a genetic algorithm was proposed. Firstly, images of cucumber seedlings in the greenhouse were collected under different light conditions, and grayscale histograms were used to evaluate the quality of target and background sample images. Secondly, the genetic algorithm was used to determine the optimal coefficient of the graying operator to further expand the difference between the grayscale of the target and background in the grayscale images. Then, the Otsu algorithm was used to perform the fast threshold segmentation of grayscale images to obtain a binary image after coarse segmentation. Finally, morphological processing and noise reduction methods based on area threshold were used to remove the holes and noise from the image, and a binary image with good segmentation was obtained. The proposed method was used to segment 60 sample images, and the experimental results show that under different lighting conditions, the average F1 score of the obtained binary images was over 94.4%, while the average false positive rate remained at about 1.1%, and the image segmentation showed strong robustness. This method can provide new approaches for the accurate identification and positioning of targets as performed by target-dosing robots in a greenhouse environment.

Suggested Citation

  • Taotao Xu & Lijian Yao & Lijun Xu & Qinhan Chen & Zidong Yang, 2023. "Image Segmentation of Cucumber Seedlings Based on Genetic Algorithm," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3089-:d:1061788
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    References listed on IDEAS

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    1. Lili Dai & He Lu & Dezheng Hua & Xinhua Liu & Hongming Chen & Adam Glowacz & Grzegorz Królczyk & Zhixiong Li, 2022. "A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
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