Agricultural pests tracking and identification in video surveillance based on deep learning

Abstract

Agricultural pests can cause serious damage to crops and need to be identified during the agricultural pest prevention and control process. In comparison with the low-speed and inefficient artificial identification method, it is important to develop a fast and reliable method for identifying agricultural pests based on computer vision. Aiming at the problem of agricultural pest identification in complex farmland environment, a recognition method through deep learning is proposed. The method could recognize and track the agricultural pests in surveillance videos of farmlands by using deep convolutional neural network and Faster R-CNN models. Compared with the traditional machine learning methods, this method has higher recognition accuracy in high background noise, and it can still effectively recognize agricultural pests with protective colorations. Therefore, compared with the current agricultural pest static-image recognition method, this method has a higher practical value and can be put into the actual agricultural production environment with the agricultural networking technology.