改进的Alexnet模型在水稻害虫图像识别中的应用

深度学习技术能以端对端方式实现农作物害虫识别,克服了传统机器学习方法特征选择具有主观性以及提取特征操作繁琐等不足,但识别的准确率和鲁棒性仍有待提高.为了研究出一种快速,高效的水稻害虫识别方法,本研究以稻纵卷叶螟、三化螟、稻蝗、稻飞虱4种常见的水稻害虫为研究对象,对传统的卷积神经网络Alexnet进行优化改进.首先从自然...

Application of Improved Alexnet in Image Recognition of Rice Pests

In order to develop a rapid and efficient identification method for rice pests, the four common rice pests, rice leaf roller, yellow rice borer, rice grasshopper and rice planthopper, were taken as the research objects to optimize and improve the traditional convolutional neural network, Alexnet. Firstly, we should obtain the images of 4 different rice pests from the natural environment and Internet, and the images should be preprocessed by methods of quantity amplification and detail enhancement. Then, the traditional convolutional neural network Alexnet is optimized and improved. Instead of using LRN for local normalization, the improved Alexnet introduces BN layer for batch normalization after each convolutional layer. In addition, global average pooling and PReLU activation function were used to optimize Alexnet. The experimental results show that: 1) The accuracy of the improved model on the pest data set is not less than 98%, which is 1.96% higher than the original network and higher than the traditional network such as LeNet5, VGG13 and VGG16. 2) The loss value of the improved model is stable around 0.03, which is 0.1 lower than the original network and lower than the traditional network such as LeNet5, VGG13 and VGG16. The experimental results show that the improved method has a higher accuracy and better robustness in the classification of rice pests, which can provide a new idea and method for the intelligent identification of crop pests