The pine wilt disease (PWD) is one of the most dangerous and destructive diseases to coniferous forests. The rapid spread trend and strong destruction directly threaten the security of forests. The complex spread pattern and the hard labor process of diagnosis call for an effective way to detect the infected areas. In this paper, an airborne edge-computing and lightweight deep learning based system are designed for PWD detection by using imagery sensors. Unmanned aerial vehicle (UAV) is firstly utilized to realize a large-scale coverage of forests, which can substantially reduce the hard labor. Except for infected trees, a large number of irrelevant images are also acquired by the UAV, which will overload the burden of process and transmission. Then a lightweight improved YOLOv4-Tiny based method (named as YOLOv4-Tiny-3Layers) is proposed to filter these uninterested images by leveraging the computation capability of edge computing, which can realize a fast coarse-grained detection with a low missing rate. Finally, all the remaining images are transmitted to the ground workstation for the final fine-grained detection. Experimental results show that the proposed system can implement a fast detection with superior performance as compared to other methods, which helps to detect the infected pine trees in a quick manner.INDEX TERMS pine wilt disease; remote sensing; airborne edge computing; lightweight deep learning; two-stage detection

Pine wilt disease (PWD) has become increasingly serious recently and causes great damage to the world’s pine forest resources. The use of unmanned aerial vehicle (UAV)-based remote sensing helps to identify pine nematode trees in time and has become a feasible and effective approach to precisely monitor PWD infection. However, a rapid and high-accuracy detection approach has not been well established in a complex terrain environment. To this end, a deep learning-based pine nematode tree identification method is proposed by fusing visible and multispectral imagery. A UAV equipped with a multispectral camera and a visible camera was used to obtain imagery, where multispectral imagery includes six bands, i.e., red, green, blue, near-infrared, red edge and red edge 750 nm. Two vegetation indexes, NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge Index) are extracted as a typical feature according to the reflectance of infected trees in different spectral bands. The YOLOv5 (You Only Look Once v5)-based detection algorithm is adopted and optimized from different aspects to realize the identification of infected pine trees with high detection speed and accuracy. e.g., GhostNet is adopted to reduce the number of model parameters and improve the detection speed; a module combining a CBAM (Convolutional Block Attention Module) and a CA (Coordinate Attention) mechanism is designed to improve the feature extraction for small-scale pine nematode trees; Transformer module and BiFPN (Bidirectional Feature Pyramid Network) structure are applied to improve the feature fusion capability. The experiments show that the mAP@0.5 of the improved YOLOv5 model is 98.7%, the precision is 98.1%, the recall is 97.3%, the average detection speed of single imagery is 0.067 s, and the model size is 46.69 MB. All these metrics outperform other comparison methods. Therefore, the proposed method can achieve a fast and accurate detection of pine nematode trees, providing effective technical support for the control of a pine nematode epidemic.