Using HOG Descriptors and UAV for Crop Pest Monitoring

Abstract

One of the major agricultural disasters, which depressed crop production and lower-quality, is pest in China. The lack of technical and scientific knowledge to prevent pest diseases is the main reason for low productivity of these crop commodities. Traditional methods based on artificial judgment have the disadvantages of large workload, low efficiency, poor working environment, and caused damage to crops. In response to the above problems, a HOG + SVM-based crop pest monitoring drone (PM-VAV) system was proposed, which utilizes computer vision combined with the flexibility of the drone for non-contact measurement. Firstly, the real aircraft working platform of this PM-UAV system was constructed. Then, through self-built data sets satisfy the requirements of the model training for pest detection task. Secondly, the on-line monitoring task of crop pests was accomplished through the use of the Histogram of Oriented Gradient (HOG) feature and Support Vector Machine (SVM) classifier, the algorithm was ported to the airborne embedded platform NVIDIA TK1. Finally, experimental tests show that the designed monitoring aircraft can effectively implement on-line monitoring for the crop pest which contained in the self-built data set.