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Special Issue:
智慧植保合辑Smart Plant Protection
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| Automatic image segmentation method for cotton leaves with disease under natural environment |
| ZHANG Jian-hua1, KONG Fan-tao1, WU Jian-zhai1, HAN Shu-qing1, ZHAI Zhi-fen2 |
1 Agricultural Information Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Big Data, Ministry of Agriculture, Beijing 100081, P.R.China
2 Chinese Academy of Agricultural Engineering, Beijing 100125, P.R.China |
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摘要 Received 25 July, 2017 Accepted 19 February, 2018
Abstract In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function ?(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour (GAC) algorithm, Chan-Vese (C-V) algorithm and Local Binary Fitting (LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.
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Received: 25 July 2017
Accepted:
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| Fund: This work is supported by the National Natural Science Foundation of China (31501229), the Chinese Academy of Agricultural Sciences Innovation Project (CAAS-ASTIP-2017-AII), and the Special Research Funds for Basic Scientific Research in Central Public Welfare Research Institutes, China (JBYW-AII-2017-05). |
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Corresponding Authors:
Correspondence KONG Fan-tao, Tel: +86-10-82105507, Fax: +86-10-82106875, E-mail: kongfantao@caas.cn
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| About author: ZHANG Jian-hua, Tel: +86-10-82106590, E-mail: zhangjianhua@caas.cn; |
Cite this article:
ZHANG Jian-hua, KONG Fan-tao, WU Jian-zhai, HAN Shu-qing, ZHAI Zhi-fen.
2018.
Automatic image segmentation method for cotton leaves with disease under natural environment. Journal of Integrative Agriculture, 17(08): 1800-1814.
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