YOLO-LF: application of multi-scale information fusion and small target detection in agricultural disease detection.

Abstract

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 and Plant Pathology 2021 - FGVC8 datasets, particularly in mAP@0.5% and mAP@0.5-0.95%, outperforming existing mainstream models. These results indicate that the proposed method effectively handles complex backgrounds and small target detection tasks in agricultural disease detection, demonstrating high practical value.

Plant phenotyping relevance

植物病斑という植物の病害状態を画像から検出・推定する深層学習モデルを開発し、複数データセットで性能評価しているため、植物フェノタイピング手法が中心です。

Code and data availability

The paper evaluates its YOLO-LF model on the public Plant Pathology 2021 - FGVC8 dataset, which is cited in the references with an explicit public Kaggle URL matching an allowed URL. No author analysis code, trained models, or other paper-specific assets are disclosed; the data availability statement only offers to 'f'