A Multi-Scale Feature Focus and Dynamic Sampling-Based Model for Hemerocallis fulva Leaf Disease Detection

Abstract

Hemerocallis fulva, essential to urban ecosystems and landscape design, faces challenges in disease detection due to limited data and reduced accuracy in complex backgrounds. To address these issues, the Hemerocallis fulva leaf disease dataset (HFLD-Dataset) is introduced, alongside the Hemerocallis fulva Multi-Scale and Enhanced Network (HF-MSENet), an efficient model designed to improve multi-scale disease detection accuracy and reduce misdetections. The Channel–Spatial Multi-Scale Module (CSMSM) enhances the localization and capture of critical features, overcoming limitations in multi-scale feature extraction caused by inadequate attention to disease characteristics. The C3_EMSCP module improves multi-scale feature fusion by combining multi-scale convolutional kernels and group convolution, increasing fusion adaptability and interaction across scales. To address interpolation errors and boundary blurring in upsampling, the DySample module adapts sampling positions using a dynamic offset learning mechanism. This, combined with pixel reordering and grid sampling techniques, reduces interpolation errors and preserves edge details. Experimental results show that HF-MSENet achieves mAP@50 and mAP%50–95 scores of 94.9% and 80.3%, respectively, outperforming the baseline model by 1.8% and 6.5%. Compared to other models, HF-MSENet demonstrates significant advantages in efficiency and robustness, offering reliable support for precise disease detection in Hemerocallis fulva.