# Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
This network leverages the initial 13 layers of VGG-16 to capture fundamental features, coupled with 6 DC layers for extracting more intricate characteristics [16]. Furthermore, a recognition algorithm for pine wilt-affected wood has been crafted around the YOLOv5 framework, integrating Mosaic data augmentation, Focus unit, and Spatial Pyramid Pooling (SPP) unit [17]. Additionally, a multi-channel convolutional neural network (CNN)-based target detection method has been employed for detecting pine wilt-diseased trees, comparing detection accuracy between the combination of each channel and the utilization of vegetation indices [18]. When dealing with image-related tasks, traditional deep learning methods encounter significant challenges due to the sheer volume of data and the necessity to consider intricate spatial information. Key among these challenges are slow training speeds and the difficulty in achieving high-quality segmentation results, especially for fine segmentation tasks. Although deep learning methods have demonstrated their effectiveness in pine nematode tree monitoring in recent years [19; 20; 21; 22; 23], traditional approaches can still lead to the loss of crucial details during the feature extraction process. This loss of details can significantly impact the accuracy of segmentation results, especially in the case of pine nematode infestation, where preserving key information is vital for subsequent segmentation tasks. Therefore, it is imperative to optimize and enhance these methods to address these issues. Fractal-wavelet analysis provides a new theoretical foundation and methodological support for the segmentation of pine wood nematode disease in images. This analysis encompasses various aspects, including Weierstrass-Mandelbrot fractal functions [24], fractional wavelet analysis, wavelet-based hyperspectral image classification, and adaptive multi-scale wavelet decomposition [25; 26; 27]. These theoretical studies offer fresh perspectives and methods for in-depth exploration in the field of image segmentation. The introduction of fractional wavelet analysis allows for a more precise capture of local features and texture information in images, significantly enhancing the accuracy and stability of pine wood nematode disease image segmentation. Furthermore, the method of hyperspectral image classification based on wavelet transforms offers new insights for feature extraction and classification of pine wood nematode disease images. Additionally, the framework of adaptive multi-scale wavelet decomposition provides an efficient solution for dealing with complex structures and textures in images. When addressing the challenges posed by small targets in UAV images, along with the issues of extensive training parameters and lengthy training cycles, this paper employs a modified U-Net architecture fused with VGG. This approach differs from the traditional U-Net by augmenting the network's layers to extract higher-dimensional feature information. By leveraging the properties of VGG's classical small convolution kernels, multiple convolution operations are performed, which significantly reduces the number of model parameters while achieving an excellent fitting effect. It has better performance than ResNet50 and DeepLab v3+.The key contributions of this paper are as follows:  
1. Increase the depth of the U-Net network to facilitate high-dimensional feature extraction. 2. Using the characteristics of a small convolutional kernel of the VGG backbone network to significantly reduce the number of parameters, thereby speeding up training and improving segmentation accuracy. This study, with a focus on Yantai in the Shandong Peninsula, aimed to devise a precise and efficient method for the rapid identification of pine trees infected with nematodes. The goal was to prevent the spread of infection to other healthy trees.