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

Tao Wang, Hongyi Xia, Jiao Xie, Jianjun Li and Junwan Liu ()
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Tao Wang: College of Computer and Mathematics, Central South University of Forestry & Technology, Changsha 410004, China
Hongyi Xia: College of Information Engineering, Hunan University of Applied Technology, Changde 415500, China
Jiao Xie: College of Computer and Mathematics, Central South University of Forestry & Technology, Changsha 410004, China
Jianjun Li: College of Computer and Mathematics, Central South University of Forestry & Technology, Changsha 410004, China
Junwan Liu: College of Computer and Mathematics, Central South University of Forestry & Technology, Changsha 410004, China

Agriculture, 2025, vol. 15, issue 3, 1-26

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 .

Keywords: Hemerocallis fulva; leaf disease detection; multi-scale feature extraction; feature fusion; upsampling (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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