MML-YOLO: A Lightweight Lesion Detector for Rice Leaf Disease Based on Enhanced YOLOv11n

  1. Chong Zhang
  2. Xiaoxi Hao
  3. Jianan Liang
  4. Lili Li
  5. Wenwei Li

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Abstract

Rice leaf disease poses a significant threat to global food security and ecological stability. While existing studies predominantly concentrate on detecting symptoms after visible lesions emerge, early-stage disease features—which are often subtle—are critical for timely intervention. This paper introduces a novel detection approach based on an enhanced YOLOv11n architecture, tailored for the precise and efficient recognition of early-stage rice leaf disease indicators. To address the limitations of traditional detection techniques in identifying fine-grained features, we propose three key modules: the Multi-branch Large-kernel Fusion Depthwise (MLFD) module, the Multi-scale Dilated Transformer-based Attention (MDTA) module, and the Lightweight Detection Head (Lo-Head). The MLFD module enhances multi-scale feature extraction via parallel pathways and depthwise convolutions with large kernels. The MDTA module integrates both spatial and channel attention through a multi-head mechanism, improving the representation of diverse lesion features. Meanwhile, the Lo-Head detection head significantly reduces model complexity and parameter count, facilitating deployment on edge devices without compromising accuracy. Experimental results show that the proposed network achieves substantial performance gains. At an input resolution of 640×640, the model reaches a mean Average Precision (mAP@50:95) of 0.7927—an increase of 1.84 percentage points over the baseline YOLOv11n. It also outperforms Faster R-CNN, YOLOv5n, YOLOv8n, and YOLOv10n by 17%, 7.2%, 3%, and 2.5% respectively, while maintaining a low computational load of 6.2 GFLOPs and 2.66M parameters. These findings underscore the model’s potential for real-world agricultural applications, particularly in enabling early detection and precise disease control. The proposed method represents a step toward proactive plant health monitoring and precision agriculture.

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    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/16902381.

    Does the introduction explain the objective of the research presented in the preprint? Yes The introduction explains the objective of this study by giving detailed information about other studies and at the end of the section thoroughly explaining the methodology followed.
    Are the methods well-suited for this research? Highly appropriate The authors provide details about the materials used in the study and the methodology which was followed.
    Are the conclusions supported by the data? Highly supported The conclusions are supported by the data, which in the results and discussion sections, are presented compared and analysed thoroughly.
    Are the data presentations, including visualizations, well-suited to represent the data? Highly appropriate and clear All data representations are correctly used, easily conveying their meaning to the reader.
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Very clearly The authors effectively communicate their findings and future next steps.
    Is the preprint likely to advance academic knowledge? Highly likely The preprint significantly contributes to the academic literature, as it demonstrates a novel method in addressing a challenging task.
    Would it benefit from language editing? No
    Would you recommend this preprint to others? Yes, it's of high quality
    Is it ready for attention from an editor, publisher or broader audience? Yes, as it is

    Competing interests

    The author declares that they have no competing interests.

  2. Version published to 10.20944/preprints202507.0700.v1