A Discriminative Pest Detection Method Based on Low-Rank Representation

Traditional manual detection method of crop pests is a quite tedious work with low efficiency, which brings great inconvenience to the control and removal of crop pests at early stage. In recently years, computer vision becomes a critical and promising technique for pest detection. However, limited to the shape and size of the pest and other issues, the perforance of these methods are not so effective and accurate. In order to improve the detection accuracy, we propose a discriminative method for pest detection on leaves based on low-rank representation and sparsity. By utilizing the lowrank characteristics of natural images, the sparsity of the noise image and the prior knowledge of color information of the crop pest images, our method decomposes the original image into low-rank image and sparse noise image, which contains all pests on the leaf. After that, the crop pests with leaf can be separate from the background and counted effectively. The experimental results show that our method can detect pests on leaf conveniently. This is of great significance for future pest judgment and management.