Recognition of Pyralidae Insects with Unmanned Monitoring Robot Based on Histogram Reverse Mapping and Invariant Moment

Abstract

Many species of Pyralidae insects are the important pests in agriculture production. However, the manual detection and identification of Pyralidae insects are labor intensive, inefficient, and subjective factors can influence recognition accuracy. To address these shortcomings, an unmanned monitoring robot car is designed. Firstly, the robot gets images by performing a fixed action and detect whether there are Pyralidae insects in the images. Secondly, the detection algorithms obtain the total probability image by using reverse mapping of histogram and multi-template images. Finally, according to the Hu moment characters, perimeter and area characters, the contours can be filtrated, and the recognition results are marked by triangles. The theoretical analysis and experimental results show that the proposed scheme has high timeliness and high recognition accuracy in the natural planting scene.