基于LM神经网络的小麦叶片病害识别

快速、及时和准确的发现小麦病害对提高小麦产量具有重要作用。以小麦叶片白粉病、条锈病和叶锈病3种病害为研究对象，提出了基于LM神经网络的小麦叶片病害识别模型。首先采用K-means算法分割小麦叶片病斑区域，提取小麦病斑区域的颜色特征和纹理特征，构建数据集。然后建立LM神经网络小麦叶片病害识别模型，输入数据进行识别。基于颜色和纹理特征的小麦叶片病害识别率为95.3%。在小样本情况下，利用LM神经网络算法能够快速、准确的识别小麦病害叶片。

Identification of Wheat Leaf Disease Based on LM Neural Network

Rapid, timely and accurate detection of wheat diseases plays an important role in improving wheat yield. Three kinds of diseased leaves suffering from wheat powdery mildew, stripe rust and leaf rust respectively were taken as the research objects, and a recognition model of wheat leaf diseases based on LM neural network was proposed. Firstly, the K-means algorithm was used to segment the wheat leaf disease area. The color features and texture features of wheat leaf disease area were extracted to construct data sets. Then, the identification model of wheat leaf disease was constructed by LM neural network and the data for identification was input. The recognition rate of wheat leaf diseases based on color and texture features was 95.3%. In the case of small samples, LM neural network algorithm can be used to identify wheat diseased leaves quickly and accurately.