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9 October 2023 Potato disease leaf segmentation model based on color matching
Yan Li, Yu Xia
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Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 1279119 (2023) https://doi.org/10.1117/12.3004670
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China
Abstract
The segmentation of potato disease leaves is of great significance to improve the accuracy of disease identification and reduce the loss of potato yield. Considering the single instance category in the image segmentation task of potato disease leaves, the classification branch in Mask-RCNN model is removed and the color matching branch is added. The K-means clustering algorithm was used to extract the RGB values and proportions of the five maximum colors of the leaf image segmented by the model. Then input the RGB values and proportions into the color matching loss function to calculate the color matching loss. Finally, the color matching loss is fused with the Mask-RCNN model’s bounding box regression loss and mask loss to form a joint loss to guide the parameter update and optimization of the entire image segmentation model. The average intersection ratio of the improved Mask-RCNN model image segmentation reaches 93.53%, which is significantly improved compared with that before the improvement. It shows that proposed method can improve the precision of the segmentation of diseased leaves, and has practical significance for the increase of potato yield.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yan Li and Yu Xia "Potato disease leaf segmentation model based on color matching", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 1279119 (9 October 2023); https://doi.org/10.1117/12.3004670
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KEYWORDS
Image segmentation
Diseases and disorders
RGB color model
Image processing algorithms and systems
Education and training
Vector spaces
Data modeling
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