基于深度卷积神经网络的玉米病害识别

为了提高玉米病害的识别率,本文提出了一种在自然环境条件下基于深度卷积神经网络的玉米病害识别方法。该方法以玉米常见的10类病害为研究对象。算法模型是先将图像预处理,应用Triplet loss双卷积神经网络结构学习玉米图像特征,再使用SIFT算法提取图像纹理细节,最后通过Softmax对图像进行标签分类。训练集采用正常玉米图像与玉米病害图像相结合的方式,使用深度相似性网络学习正常玉米图像特征表示,再使用迁移学习方法学习玉米病害图像的特征,最后对特征进行分类识别。研究结果表明,该方法可准确识别10种常见玉米病害,正确率可达90%以上,为玉米病害的防治提供了有效的技术支持。

Maize Diseases Identification Based on Deep Convolutional Neural Network

To improve the identification rate of maize diseases, an identification method of diseases is established based on a deep convolutional neural network under natural environment condition. Ten common maize diseases were taken as the study objects, and the image preprocessing was carried out, the Triplet loss double convolution neural network structure was applied to study the features of maize images. Then, SIFT algorithm was adopted to extract textural features. By this way, the labeling and classification of images through the Softmax were conducted. The normal maize images and the diseased ones were combined by the training set. Deep learning on similarity network check was used to explore the features of normal maize images, and furthermore, transfer learning was used to delve into the features of diseased maize images. The features were classified and identified. The result shows that this method could accurately identify the ten common maize diseases, and the classification accuracy is over 90%.