Crop leaf disease detection with additive gated convolution and hierarchical attention fusion

Crop leaf disease detection plays a crucial role in ensuring healthy crop growth and improving food security. Disease features are often small and have blurry edges, while background interference is strong, making precise detection a significant challenge. Although YOLO-based methods perform well in object detection, they still struggle to effectively handle the extraction of lesion details and background noise interference when applied to crop leaf disease detection. To address these challenges, this study introduces an innovative crop leaf disease detection approach built upon the YOLO, named AG-HAF. This method proposed two modules, the additive gated convolutional unit (AGCU), which introduces a gating mechanism to dynamically adjust the importance of features, enhancing the detection of small and blurry lesions, improving nonlinear feature modeling, and suppressing irrelevant background interference. Hierarchical Attention Fusion Module (HACFM), which utilizes a hierarchical attention mechanism to optimize the fusion of multi-scale features, enhancing the representation and semantic information of disease regions, and further improving the model's adaptability to complex backgrounds. Ablation and comparative experiments show that AG-HAF outperforms existing methods across various metrics, particularly excelling in disease detecting in complex backgrounds and small lesions, demonstrating significant potential for practical applications.