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Proceedings Paper

Leaf blast spot detection method based on Linknet
Author(s): Chen Junshen; Gong Meiling; Dong Wang; Xiaoxia Zhao; Min Song; Huimin Guo
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Paper Abstract

This paper aims to solve the problem of automatic detection of rice leaf lesions in natural scenes using deep learning techniques. In this paper, the Linknet full convolutional network was built to train the segmentation model. The network compensates the lost spatial information in the feature extraction process through the short connection structure between downsampling and corresponding upsampling. The model takes rice canopy RGB image as input and then output binarized lesion segmentation image. Then considered with the distribution characteristics of lesion spots, the loss function of the origin model was replaced with Focal loss function, which further improved the segmentation accuracy of the model. The average precision and recall have respectively achieved 98.55% and 98.64% on validate data set, and the average false positive rate has reduced to 1.36%, which has a better segmentation performance. It creates a good precondition for automatic identification of leaves diseases.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 1142742 (31 January 2020);
Show Author Affiliations
Chen Junshen, Beijing Aerospace FT Equipment Technology Co., Ltd. (China)
Gong Meiling, Beijing Aerospace FT Equipment Technology Co., Ltd. (China)
Dong Wang, Beijing Aerospace FT Equipment Technology Co., Ltd. (China)
Xiaoxia Zhao, Beijing Aerospace FT Equipment Technology Co., Ltd. (China)
Min Song, Beijing Aerospace FT Equipment Technology Co., Ltd. (China)
Huimin Guo, Beijing Aerospace FT Equipment Technology Co., Ltd. (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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