A novel strategy for pest disease detection of Brassica chinensis based on UAV imagery and deep learning

Abstract

ABSTRACT Pest disease is considered to be among the greatest threats to Brassica chinensis. The physical invasion caused by pests will form wormholes, which seriously affect the yield and quality of Brassica chinensis. To tackle this problem, we innovatively propose automatic pest disease detection by taking images using unmanned aerial vehicle (UAV). Compared to the traditional on-site manual inspection and fixed camera-based image detection methods, it has the advantages of high efficiency, low cost, and suitable for a wide range of scenes. However, pest disease detection through UAV images faces many challenges, such as image blur and small object size, and it is difficult for previous methods to achieve accurate and effective detection. Therefore, we propose a strategy for pest disease detection of Brassica chinensis in aerial images based on deep learning. At first, we employed an image tiling module to preprocess the aerial images. Then, an image restoration module is adopted to deblur the image. Third, an advanced method of disease object detection based on improved CenterNet that combines attention mechanism and DIoU loss is proposed. In addition, a dataset of aerial images of Brassica chinensis bitten by flea beetle was built and annotated, containing 42,452 wormhole annotations. Extensive comparative experimental results show that the detection performance of the proposed method is superior to the existing detection methods. And we achieved an overall accuracy of 87.2% AP50 and 94.7% R-squared for pest disease detection of Brassica chinensis, which can meet the requirements of real application.