Precise identification of water bodies in agricultural watersheds is crucial for irrigation, water resource management, and flood disaster prevention. However, the spectral noise caused by complex light and shadow interference and water quality differences, combined with the diverse shapes of water bodies and the high computational cost of image processing, severely limits the accuracy of water body recognition in agricultural watersheds. This paper proposed a lightweight and efficient learnable Kalman filter and Deformable Convolutional Attention Network (LKF-DCANet). The encoder is built using a shallow Channel Attention-Enhanced Deformable Convolution module (CADCN), while the decoder combines a Convolutional Additive Token Mixer (CATM) and a learnable Kalman filter (LKF) to achieve adaptive noise suppression and enhance global context modeling. Additionally, a feature-based knowledge distillation strategy is employed to further improve the representational capacity of the lightweight model. Experimental results show that LKF-DCANet achieves an Intersection over Union (IoU) of 85.95% with only 0.22 M parameters on a public dataset. When transferred to a self-constructed UAV dataset, it achieves an IoU of 96.28%, demonstrating strong generalization ability. All experiments are conducted on RGB optical imagery, confirming that LKF-DCANet offers an efficient and highly versatile solution for water body segmentation in precision agriculture.