Addressing the challenges posed by the small scale, diverse morphology, and severe occlusion of pest and disease targets on lotus leaves in complex aquatic environments—as well as the difficulty of existing methods in automatically quantifying disease severity—this paper proposes an approach for the identification of lotus leaf pests and diseases and quantitative grading of leaf spot disease severity. First, by integrating high-altitude canopy imagery captured by unmanned aerial vehicles (UAVs) with high-definition ground-level data, a multi-perspective dataset comprising object detection bounding box annotations and pixel-level segmentation annotations is constructed. Second, the YOLOv12n-DFFN object detection model is proposed; this model enhances interaction between deep and shallow features through a dynamic feature feedback mechanism, thereby improving the ability to localize disease targets against complex aquatic backgrounds. Finally, taking typical leaf spot disease as the subject, YOLOv12n-DFFN is used to detect and extract diseased leaf regions, while a VGG-UNet semantic segmentation model is employed to achieve pixel-level fine-grained segmentation of leaf and lesion areas. By calculating the ratio of lesion area to total leaf area, automatic quantitative grading of disease severity is realized. Experimental results show that YOLOv12n-DFFN achieved a precision of 93.58%, representing an improvement of 4.37 percentage points over the YOLOv12n baseline, with an mAP50 of 83.87%; the segmentation model attained an average intersection-over-union of 90.47%, and the overall accuracy of the two-stage framework for leaf spot disease severity grading reached 96.0%. Through a “detection first, segmentation second” two-stage strategy, this framework enables the intelligent identification and severity quantification of lotus leaf diseases in complex aquatic environments, providing an effective approach for the intelligent monitoring and precision management of aquatic crop diseases.