Chinese Academy of Agricultural Mechanization

Beijing, China

Chinese Academy of Agricultural Mechanization

Beijing, China
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Bo Z.,Chinese Academy of Agricultural Mechanization | Hua W.H.,Chinese Academy of Agricultural Mechanization | Jun L.S.,Chinese Academy of Agricultural Mechanization | Hua M.W.,Chinese Academy of Agricultural Mechanization | Chao Z.X.,Chinese Academy of Agricultural Mechanization
Proceedings of the World Congress on Intelligent Control and Automation (WCICA) | Year: 2015

A new method of weed recognition based on the invariant moments was proposed in this paper. Firstly, the area of the soybean leaf was located from the complicated image background. Secondly, the features of soybean leaf were obtained by Hu invariant moments, which are the invariability of the translation, the ratio and the rotation, and have lower computational complexity. Finally, the soybean leaf was recognized by the nearest neighbor classifier, and other image information were identified to weed. Experimental results proved that the weed recognition method was effective on the different environment, and could location the weed rapidly, reliably and accurately. The correct rate of the weed recognition was 90.5% in the ordinary environment, the average cost time was 670ms. © 2014 IEEE.


Hua W.H.,Chinese Academy of Agricultural Mechanization | Bo Z.,Chinese Academy of Agricultural Mechanization | Jun L.S.,Chinese Academy of Agricultural Mechanization | Hua M.W.,Chinese Academy of Agricultural Mechanization | Chao Z.X.,Chinese Academy of Agricultural Mechanization
Proceedings of the World Congress on Intelligent Control and Automation (WCICA) | Year: 2015

An automatic detection system for cotton field information is designed, which could automatically acquisition cotton field video and analysis insect pest and seedling situation including color, morphology, size, egg and coverage of cotton leaves. Experimental results prove that the new system was effective on remote analysis and process of pest and seedling information. © 2014 IEEE.


Zhao B.,Chinese Academy of Agricultural Mechanization | Wang Z.,Chinese Academy of Agricultural Mechanization | Zhou P.,Chinese Academy of Agricultural Mechanization | Mao W.,Chinese Academy of Agricultural Mechanization | Zhang X.,Chinese Academy of Agricultural Mechanization
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2012

In order to reduce the application amount of pesticide, the intelligent weed recognition system was designed, which was composed of intelligent controller, nozzle assembly and installing bracket, and a new weed recognition method based on color and location characteristics was applied to the different environment in this paper. Experimental results showed that the intelligent weed recognition system was effective in the different environments, and could locate the weed rapidly, reliably and accurately. The recognition correct rate of the intelligent weed recognition system was 97.0% in the ordinary environment, 92.5% in strong illumination environment, and 89.2% in the shadow environment. The average recognition time was 160 ms. The research can provide a reference for design of the precise spraying weeding system based on machine vision.


Zhao B.,Chinese Academy of Agricultural Mechanization | Fan Y.,Chinese Academy of Agricultural Mechanization | Mao W.,Chinese Academy of Agricultural Mechanization | Zhou P.,Chinese Academy of Agricultural Mechanization | Zhang X.,Chinese Academy of Agricultural Mechanization
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2012

In order to improve the adaptability of the vision navigation system in the agricultural vehicle to various environments, the path recognition method for different illumination environments was proposed in this paper. Firstly, the influence of different illumination environments on the path recognition for the vision navigation system of the agricultural vehicle was investigated. Secondly, on basis of path recognition in the ordinary environment, a new method mainly composed of histogram equalization algorithm and center line detecting algorithm was applied in the path recognition in different illumination environment. Finally, the many images of different illumination environment were used to verify proposed method. Experimental results showed that the new method was effective in various illumination environments, and could obtain the target navigation path reliably and accurately. The recognition correct rate of the vision navigation system was 92%, and the average cost time was 245 ms. The research can provide a reference for design of the vision navigation system in the agricultural vehicle.

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