Image Segmentation of Cucumber Seedlings Based on Genetic Algorithm

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 c...

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Bibliographic Details
Published in:Sustainability 2023-02, Vol.15 (4), p.3089
Main Authors: Xu, Taotao, Yao, Lijian, Xu, Lijun, Chen, Qinhan, Yang, Zidong
Format: Article
Language:English
Subjects:
Agricultural production
Algorithms
Cameras
Cucumbers
Deep learning
Dosage
Genetic algorithms
Gray scale
Greenhouses
Histograms
Identification
Image processing
Image quality
Light
Methods
Morphology
Noise control
Noise reduction
Robots
Seedlings
Sustainability
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su15043089