## 1 Introduction
Aiming at the problem of insufficient multi-scale feature perception ability, researchers have proposed a variety of improvement schemes to address key challenges in water body identification, such as interference from complex backgrounds, the lack of connectivity of large-scale water areas, and rough edge positioning. [PERSON] et al. [23] proposed MSCEnet, which effectively integrates multi-scale contextual information by embedding the Res2 Net [24] module, dilated convolutions, and multi-kernel convolution units. [PERSON] et al. [25] developed a water body extraction method for remote sensing images, which employs a dual attention module to enhance the global dependencies in both spatial and channel dimensions. To enhance the ability to extract dense scale features, [PERSON] et al. introduced an improved DensePPM module [26]. In addition, to address the challenge of extracting water body boundaries, [PERSON] et al. [27] proposed a hybrid semantic segmentation strategy based on K-Net, which improves the segmentation accuracy of lakes by iteratively optimizing the details. [PERSON] et al. [28] proposed MSFENet, which reduces the dependence on large-scale data by extracting multi-scale features of water bodies and combining contrastive learning. FWE-net [29] and MEC-net [30], through the channel attention mechanism, adaptively adjust the weights of feature channels and guide the model to focus on the edge areas of water bodies. Despite the progress achieved by the aforementioned methods in water body extraction, conventional convolutional neural networks still face limitations when addressing the unique challenges of agricultural watersheds--such as complex terrain, significant variations in water body scales, and diverse vegetation coverage. In recent years, the Transformer network has become an important technology for solving the problem of complex feature correlations in water body extraction due to its self-attention mechanism. Vision Transformer (ViT) [31] achieves powerful capture of global dependencies through image partitioning and sequence modeling. [PERSON] et al. [32] proposed a multi-level Transformer module that establishes long-range dependencies between the encoder and the decoder, enhancing the context modeling ability of the boundary information of lakes. With the advancement of general-purpose segmentation frameworks, vision-language pretraining models such as CLIP [33] and Segment Anything Model (SAM) [34] have demonstrated remarkable generalization capabilities across diverse segmentation tasks by leveraging large-scale cross-modal training. However, when applied to domain-specific tasks such as water body identification in agricultural watersheds, they still lack effective adaptation strategies. Although Transformer-based architectures have demonstrated superior performance to traditional CNNs in semantic segmentation tasks, the computational cost of their self-attention mechanism increases quadratically with the sequence length [35], which severely restricts their application on intelligent agricultural devices. Therefore, there is an urgent need to develop lightweight solutions that meet practical requirements. To this end, various techniques, such as low-rank decomposition, model pruning, weight quantization, and knowledge distillation, have been proposed to reduce the redundancy existing in deep neural network architectures [36; 37]. [PERSON] et al. [38], the proposers of UNet++, pointed out that by drawing on the design examples of PSPNet [18] and FC-DenseNets [39], shallow downsampling can achieve better performance than deep networks under certain circumstances. EDANet [40], on the other hand, utilizes the asymmetric convolutional dense module to decompose the n \(\times\) n convolution into n \(\times\) 1 and 1 \(\times\) n kernels, thus improving the segmentation efficiency.