# Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
)1 School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China; [EMAIL_ADDRESS] (J.S. )1 School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China; [EMAIL_ADDRESS] (J.S. )1 School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China; [EMAIL_ADDRESS] (J.S. )1 School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China; [EMAIL_ADDRESS] (J.S. )1 School of Mathematics and Information Sciences, Yantai University, Yantai 264003, China; [EMAIL_ADDRESS] (J.S. )1 School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China; [EMAIL_ADDRESS] (J.S. )1 School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China; [EMAIL_ADDRESS] (J.S. )1 School of Mathematics and Information Sciences, Yantai University, Yantai 264003, China; [EMAIL_ADDRESS] (J.S. )1 School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China; 13280062738@163. Pine wood nematode disease primarily arises from the parasitism of pine wood nematodes, which are transmitted within pine trees by vector insects like the pine beetles. The disease typically progresses through four distinct stages: an initial stage where trees appear normal, followed by needle discoloration, then wilting of the tree, and ultimately, the death of the entire plant. When it comes to preventing and controlling pine wilt disease, monitoring the epidemic is crucial. In China, initial monitoring efforts primarily relied on manual surveys. However, due to limitations posed by roads, terrain, and various other complex factors, these methods often resulted in inefficient work productivity and limited effectiveness in epidemic prevention and control. As science and technology progress, the employment of remote sensing technology to detect pine wood nematode-infested trees has emerged as a crucial technical approach for monitoring the spread of pine wood nematode disease. The primary foundation for identifying pine wilt disease-affected trees through remote sensing lies in the alterations in the spectral properties of plants, stemming from changes in water and chlorophyll content within infected specimens [5]. Studies focusing on remote sensing monitoring of pine wood nematode disease began to emerge in the 1990s [6], encompassing the utilization of spectroradiometers to measure spectral curves of diverse pine tree types infected with pine wilt disease, conducting differential analyses on these curves, as well as measuring reflectance spectra at various stages of infection and analyzing the associated spectral characteristics and chlorophyll variations [7]. Numerous investigations into the spectral features of pine wilt disease-infected trees have confirmed that analyzing remotely sensed data can effectively lead to the identification of diseased trees [8]. However, the relatively low resolution of early remote sensing satellites often leads to reduced accuracy in results and the potential omission of diseased trees [9]. Machine learning-based image segmentation techniques offer remarkable speed and precision, making them a popular choice for target recognition and segmentation in high-resolution remote sensing images [10]. Previous research has validated the effectiveness of these methods. For instance, traditional support vector machines (SVM) [11; 12] have achieved enhanced results, while a study focusing on individual pine tree identification employed a modified LeNet architecture [13]. Furthermore, a stochastic deep forest algorithm was implemented to monitor pine nematode-infested trees [14]. Another approach for detecting pine diseases involves utilizing orthophoto-corrected 5-band multispectral images from unmanned aerial vehicles (UAVs), paired with a ResNet18 backbone network augmented with a modified DenseNet module for classification [15]. The VDNet network, a fusion of VGG-16 and dilated convolution (DC), was also developed for pine disease detection.