The accurate pest control of pear tree diseases is an urgent need for the realization of smart agriculture, with one of the key challenges being the precise segmentation of pear leaf diseases. However, existing methods show poor segmentation performance due to issues such as the small size of certain pear leaf disease areas, blurred edge details, and background noise interference. To address these problems, this paper proposes an improved U-Net architecture, FFAE-UNet, for the segmentation of pear leaf diseases. Specifically, two innovative modules are introduced in FFAE-UNet: the Attention Guidance Module (AGM) and the Feature Enhancement Supplementation Module (FESM). The AGM module effectively suppresses background noise interference by reconstructing features and accurately capturing spatial and channel relationships, while the FESM module enhances the model's responsiveness to disease features at different scales through channel aggregation and feature supplementation mechanisms. Experimental results show that FFAE-UNet achieves 86.60%, 92.58%, and 91.85% in MIoU, Dice coefficient, and MPA evaluation metrics, respectively, significantly outperforming current mainstream methods. FFAE-UNet can assist farmers and agricultural experts in more effectively evaluating and managing diseases, thereby enabling precise disease control and management.