Rapid Crop Insurance Survey Method Based on Unmanned Aerial Vehicle Imagery

The rapid crop insurance survey method based on unmanned aerial vehicle imagery mainly relies on deep learning object detection to identify rice fields in the images captured by the drone and accurately estimate their area, achieving fast surveying for agricultural insurance. This research is beneficial in promoting modern technology to support agriculture, accelerating the modernization process of agriculture, and facilitating government personnel to verify the area of rice planting by farmers. In this study, three methods, namely Alex Net, VGG, and Res Net, were used for image classification and recognition. The final result showed that the effectiveness of the Alex Net network was superior to that of VGG and Res Net, with a recognition accuracy of rice fields exceeding 90%, meeting the precision requirements of crop insurance surveying, indicating that the research has certain significance and can improve the efficiency of crop insurance surveying.