Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases.

葡萄病害是导致葡萄产量下降的一个重要因素，通常首先影响叶子。葡萄叶病的有效鉴定仍然是一个关键的未满足需求。为了减轻葡萄叶片特征提取中的背景干扰，提高提取小病斑的能力，本研究结合葡萄叶片病害的特征，开发了一种新的病害识别和分类方法。首先，采用高斯滤波器Sobel平滑去噪拉普拉斯算子（GSSL）来降低图像噪声并增强葡萄叶的纹理。随后将一种新的网络称为协调注意洗牌机制-非对称多尺度融合模块网（CASM-AMFMNet）应用于葡萄叶病识别。采用 CoAtNet 作为网络骨干，提高模型学习和泛化能力，在一定程度上缓解了梯度爆炸问题。CASM-AMFMNet 进一步用于捕获和定位葡萄叶病害区域，从而减少背景干扰。最后，利用非对称多尺度融合模块（AMFM）从葡萄叶片上的小病斑中提取多尺度特征，以准确识别小目标病害。基于我们自制的葡萄叶图像数据集的实验结果表明，与现有方法相比，CASM-AMFMNet 的准确率达到了 95.95%，F1 得分达到了 95.78%，mAP 达到了 90.27%。全面的，