摘要
Recognition and analysis of dynamic information about population images during wheat growth periods can be taken for the base of quantitative diagnosis for wheat growth. A recognition system based on self-learning BP neural network for feature data of wheat population images, such as total green areas and leaves areas was designed in this paper. In addition, some techniques to create favorable conditions for image recognition was discussed, which were as follows: (1) The method of collecting images by a digital camera and assistant equipment under natural conditions in fields. (2) An algorithm of pixel labeling was used to segment image and extract feature. (3) A high pass filter based on Laplacian was used to strengthen image information. The results showed that the ANN system was availability for image recognition of wheat population feature.
Recognition and analysis of dynamic information about population images during wheat growth periods can be taken for the base of quantitative diagnosis for wheat growth. A recognition system based on self-learning BP neural network for feature data of wheat population images, such as total green areas and leaves areas was designed in this paper. In addition, some techniques to create favorable conditions for image recognition was discussed, which were as follows: (1) The method of collecting images by a digital camera and assistant equipment under natural conditions in fields. (2) An algorithm of pixel labeling was used to segment image and extract feature. (3) A high pass filter based on Laplacian was used to strengthen image information. The results showed that the ANN system was availability for image recognition of wheat population feature.
作者
LI Shao-kun, SUO Xing-mei, BAI Zhong-ying, QI Zhi-li, Liu Xiao-hong, GAO Shi-ju and ZHAO Shuang-ning( Institute of Crop Breeding and Cultivation /Key Laboratory of Crop Genetic & Breeding, Ministry of Agriculture, ChineseAcademy of Agricultural Sciences, Beijing 100081 , P . R . China
Department of Computer Science and Technology, CentralUniversity for Nationalities, Beijing 100081 , P. R . China
School of Computer Science and Technology, Beijing Universityof Posts and Telecommunications, Beijing 100876, P. R . China
Research Center of Xinjiang Crop High-yield,Shihezi University, Shihezi 832003, P.R. China)
基金
suppported by the National Nat-ual Sience Fundation of China(990427 and“863”Opening Item(001A110-02)
作者简介
LI Shao-kun (1963-), Professor, Ph D, Tel:86-10-68918891, E-mail: shaokun0004@sina.com.cn
