Convolutional neural network based automatic pest monitoring system using hand-held mobile image analysis towards non-site-specific wild environment

Abstract

Due to cost-effectiveness and efficient automation, image analytic based automatic pest monitoring techniques are widely utilized in specialized control of pests in the agricultural crops industry. They could achieve good recognition performance in certain species of pests in site-specific environment, but are sensitive to content and characteristics of image like appearance variances and clustered background. For pests with small size and indistinct features like rice planthopper, it is hard to manually and timely select suitable features. In this paper, we propose an effective CNN based automatic hand-held mobile pest monitoring system to detect and count rice planthoppers. A rice planthopper search network (RPSN) approach is proposed for automatically extracting multiple high-quality proposal regions from large-scale pest images with tiny objects. Additionally, sensitive score matrix (SSM) is employed to further enhance the performance of classification and bounding box regression. The experimental results under the proposed approaches evaluating three types of density pest images show that our system performs well on detecting rice planthoppers in non-specific wild environment with recognition recall up to 91% in industrial circumstance, which outweighs the state-of-the art approaches.