基于深度卷积神经网络的储粮害虫图像识别

目的]为了防治储粮害虫带来的危害,借助计算机对储粮害虫进行有效的图像识别是具有重要意义的。方法]针对基于图像的储粮害虫多分类识别问题,引入了基于深度卷积神经网络的储粮害虫图像识别方法。该方法与传统的储粮害虫识别方法相比,大幅度简化了数据预处理过程,结果]同时该方法在识别精确度方面达到了97.61%,也明显优于传统方法。结论]因此基于深度卷积神经网络的储粮害虫识别方法具有较高的实用性,且具有进一步研究和推广的意义。

Image Recognition of Stored Grain Pests: Based on Deep Convolutional Neural Network

In order to prevent and control the damage caused by stored grain pests, it is of great significance to find an effective way of recognizing stored grain pests with the help of computer. Focusing on the problems of multi-class recognition of stored grain pests based on images, a new image recognition method of stored grain pests based on deep convolutional neural network was proposed. Compared with the traditional ways of stored grain pests recognition, this new method greatly simplified the data preprocessing process, and the accuracy of this new method achieved 97.61%, which was significantly superior to the traditional methods. Therefore, the method of recognizing stored grain pests based on deep convolutional neural network has high practicability as well as the significance of further research and promotion.