Pest identification via deep residual learning in complex background

Abstract

Agricultural pests severely affect both agricultural production and the storage of crops. To prevent damage caused by agricultural pests, the pest category needs to be correctly identified and targeted control measures need to be taken; therefore, it is important to develop an agricultural pest identification system based on computer vision technology. To achieve pest identification with the complex farmland background, a pest identification method is proposed that uses deep residual learning. Compared to support vector machine and traditional BP neural networks, the pest image recognition accuracy of this method is noticeably improved in the complex farmland background. Furthermore, in comparison to plain deep convolutional neural networks such as Alexnet, the recognition performance in this method was further improved after optimized by deep residual learning. A classification accuracy of 98.67% for 10 classes of crop pest images with complex farmland background was achieved. Accordingly, the method has a high value of practical application, and can be integrated with currently used agricultural networking systems into actual agricultural pest control tasks.