ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1609284
YOLO-LF: Application of Multi-Scale Information Fusion and Small Target Detection in Agricultural Disease Detection
Provisionally accepted
- Putra Malaysia University, Selangor Darul Ehsan, Malaysia
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With the increasing threat of agricultural diseases to crop production, traditional manual detection methods are inefficient and highly susceptible to environmental factors, making an efficient and automated disease detection method urgently needed. Existing deep learning models still face challenges in detecting small targets and recognizing multi-scale lesions in complex backgrounds, particularly in terms of multi-feature fusion. To address these issues, this paper proposes an improved YOLO-LF model by introducing modules such as CSPPA (Cross-Stage Partial with Pyramid Attention), SEA (SeaFormer Attention), and LGCK (Local Gaussian Convolution Kernel), aiming to improve the accuracy and efficiency of small target disease detection. Specifically, the CSPPA module enhances multi-scale feature fusion, the SEA module strengthens the attention mechanism for contextual and local information to improve detection accuracy, and the LGCK module increases the model's sensitivity to small lesion areas. Experimental results show that the proposed YOLO-LF model achieves significant performance improvements on the Plant Pathology 2020 -FGVC7
Keywords: YOLO-LF, Small target detection, Agricultural disease detection, multi-scale information fusion, deep learning, YOLOv11
Received: 10 Apr 2025; Accepted: 12 Aug 2025.
Copyright: © 2025 Wang, Tang, Mohd Ariffin, Mohd Ariffin and shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xinming Wang, Putra Malaysia University, Selangor Darul Ehsan, Malaysia
Sai Hong Tang, Putra Malaysia University, Selangor Darul Ehsan, Malaysia
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