基于BP神经网络的小麦群体图像特征识别  

The Machine Recognition for Population Feature of Wheat Images Based on BP Neural Network

作　　者：李少昆[1,2] 索兴梅[3] 白中英[4] 祁之力[4] 刘晓鸿[4] 高世菊[1] 赵双宁[1] 

机构地区：[1]中国农业科学院作物育种栽培研究所 [2]石河子大学新疆作物高产研究中心,石河子832003 [3]中央民族大学计算机科学与技术系 [4]北京邮电大学计算机科学与技术学院

出　　处：《中国农业科学》2002年第6期616-620,共5页Scientia Agricultura Sinica

基　　金：国家自然科学基金资助项目 (3 9970 42 7) ;国家"863"课题资助项目 (863 3 0 6 ZD0 5 0 1 9)

摘　　要：小麦群体特征指标是生产上判断生长动态是否合理和因苗管理的主要依据。以小麦群体绿色面积和绿色叶面积指标信息的获取为例 ,研究了基于图像信息构建自学习BP神经网络识别模型的技术。在用数码相机拍摄小麦群体图像 ,用像素标记算法进行图像分割和特征提取 ,用基于拉普拉斯算子的高通增强滤波技术进行图像增强处理的基础上 ,通过构建的BP人工神经网络 (ANN)模型实现了群体指标的识别 ,准确率在 85 %以上 。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.

关 键 词：BP神经网络 小麦 群体图像 特征识别 绿叶面积 图像识别 

分 类 号：S512.1[农业科学—作物学] S126