# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 1 Introduction  
Potatoes are a crucial crop in many countries, profoundly impacting both local and global agricultural economies. They are a significant source of income for farmers and provide ample opportunities for the food processing and trading industries [1]. Consequently, the stable development of the potato industry is paramount for enhancing farmer incomes and promoting socio-economic growth. Presently, the detection of potato leaf diseases primarily relies on visual inspection by farmers or experts, identifying disease symptoms by observing the potato plant's leaves [2]. While this method offers convenience in field operations, it requires professional knowledge and experience for accurate disease identification [3]. If leaf diseases are not promptly detected and managed, adverse effects on potato yield and quality can occur, harming farmers' economic interests [4, 5]. Deep learning models, trained on extensive labeled samples, can achieve automated, efficient, and precise detection of leaf diseases [6]. The application of such technology aids in the early detection of diseases, allowing farmers to take timely and appropriate preventive measures, safeguarding crop health, and improving yields and agricultural productivity. Numerous researchers have extensively studied plant leaf segmentation and identification in recent years. [PERSON] et al. [7] proposed a leaf segmentation network, ULeaf-Net,based on a U-shaped symmetric encoder-decoder architecture. This network employs cross-layer feature fusion to enhance segmentation results. [PERSON] et al. [8] introduced a UNet persimmon leaf disease image segmentation based on a self-attention mechanism and discernible convolution. They used UNet as the primary network, applied variable convolution networks for downsampling to enhance feature extraction, and utilized self-attention mechanisms to acquire more spatial and contextual information, achieving an MIOU value of 83.58%. [PERSON] [9] and his colleagues suggested differential evolution for segmenting diseased regions on leaves, aiming to reduce training time and space. [PERSON] [10] and team put forward the application of Deeplab v3+ for segmenting potato leaves and then combined texture features with VGG16 for feature extraction to build a convolutional neural network for disease identification. [PERSON] et al. [11] presented a wheat Fusarium head blight semantic segmentation network model, UNetA, that integrated convolutional neural networks and attention mechanisms. They used a weighted cross-entropy loss function to measure the disparity between predicted and actual values while mitigating sample imbalance issues, achieving an MIOU value of 83.90%. [PERSON] et al. [12] introduced an improved U-Net strawberry semantic segmentation model based on the attention mechanism. They incorporated a CNN-Transformer hybrid structure in the encoder and replaced the traditional upsampling with a dual upsampling module in the decoder, achieving a segmentation accuracy of 92.56%. However, there were challenges with high computational demands and extended training times. U-Net, which combines low-detail and high-semantic information, has impressive performance in biomedical image segmentation [13; 14]. Deep residual units make deep networks easy to train, and in-network skip connections assist in transmitting information without degradation [15; 16]. Inspired by the advantages of U-Net and MultiResUNet [17], an improved UNet (RS-Net) was developed for the image segmentation of diseased potato leaves. Despite the impressive performance of various advanced models in plant disease recognition research, significant challenges persist. Existing models exhibit diversity in segmentation performance when dealing with different datasets, tiny disease spot features, fuzzy boundaries, and morphological variations. To address these challenges, this paper proposes a potato leaf disease segmentation method based on an improved UNet network.