【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 ,表明利用ANN技术对小麦群体特征图像识别是可行的更多还原

【Abstract】 The recognition and analysis of dynamic information about population images during wheat growth period 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 were discussed, which were as follows: 1.The method of collecting images by a digital camera and an 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 Laplace was used to strengthen image information. The results showed that the ANN system was availability for image recognition of wheat population feature.更多还原