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Mamba-YOLO-ML: A State-Space Model-Based Approach for Mulberry Leaf Disease Detection

文献类型： 外文期刊

作者： Yuan, Chang 1 ; Li, Shicheng 1 ; Wang, Ke 2 ; Liu, Qinghua 2 ; Li, Wentao 2 ; Zhao, Weiguo 3 ; Guo, Guangyou 2 ; Wei, Lai 2 ;

作者机构： 1.Jiangsu Univ Sci & Technol, Sch Comp, Zhenjiang 212100, Peoples R China

2.Jiangsu Univ Sci & Technol, Sch Automat, Zhenjiang 212100, Peoples R China

3.Jiangsu Univ Sci & Technol, Sch Biotechnol, Jiangsu Key Lab Sericultural Biol & Biotechnol, Zhenjiang 212100, Peoples R China

4.Chinese Acad Agr Sci, Sericultural Res Inst, Key Lab Silkworm & Mulberry Genet Improvement, Minist Agr & Rural Affairs, Zhenjiang 212100, Peoples R China

关键词： mulberry leaf disease; Mamba; Haar wavelet; SSM; object detection

期刊名称：PLANTS-BASEL （ 影响因子：4.1； 五年影响因子：4.5 ）

ISSN： 2223-7747

年卷期： 2025 年 14 卷 13 期

页码：

收录情况： SCI

摘要： Mulberry (Morus spp.), as an economically significant crop in sericulture and medicinal applications, faces severe threats to leaf yield and quality from pest and disease infestations. Traditional detection methods relying on chemical pesticides and manual observation prove inefficient and unsustainable. Although computer vision and deep learning technologies offer new solutions, existing models exhibit limitations in natural environments, including low recognition rates for small targets, insufficient computational efficiency, poor adaptability to occlusions, and inability to accurately identify structural features such as leaf veins. We propose Mamba-YOLO-ML, an optimized model addressing three key challenges in vision-based detection: Phase-Modular Design (PMSS) with dual blocks enhancing multi-scale feature representation and SSM selective mechanisms and Mamba Block, Haar wavelet downsampling preserving critical texture details, and Normalized Wasserstein Distance loss improving small-target robustness. Visualization analysis of the detection performance on the test set using GradCAM revealed that the enhanced Mamba-YOLO-ML model demonstrates earlier and more effective focus on characteristic regions of different diseases compared with its predecessor. The improved model achieved superior detection accuracy with 78.2% mAP50 and 59.9% mAP50:95, outperforming YOLO variants and comparable Transformer-based models, establishing new state-of-the-art performance. Its lightweight architecture (5.6 million parameters, 13.4 GFLOPS) maintains compatibility with embedded devices, enabling real-time field deployment. This study provides an extensible technical solution for precision agriculture, facilitating sustainable mulberry cultivation through efficient pest and disease management.

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