Abstract: In order to solve the problem of difficult and effective detection of rice diseases, a rice disease detection algorithm called DNC-YOLOv7 was proposed, focusing on key diseases such as bacterial blight, rice blast, and brown spot. Firstly, in response to the shortcomings of YOLOv7's original upsampling module in extracting semantic information of rice diseases, the NC (Nearest CARAFE) upsampling module was introduced. This module significantly improved the ability of the network model in restoring the details of rice leaf images, so that the model could more accurately capture and identify disease features. Secondly, in order to further enhance the feature extraction and fusion capabilities of the model, the DFPN structure was proposed to improve the neck design of the original model. Finally, Mixup and Mosaic techniques were used to enhance the original dataset to enhance the model's generalization ability and robustness. The experimental results show that the average detection accuracy of DNC-YOLOv7 algorithm on the dataset is significantly improved from the original 83.4% to 93.2%, which is 9.8% higher than the traditional YOLOv7 algorithm.

Key words: rice leaves, disease detection, YOLOv7, data augmentation, CARAFE

摘要： 为解决水稻病害难以有效检测的问题，以水稻白叶枯病、稻瘟病、褐斑病等关键病害为研究对象，提出一种名为DNC-YOLOv7的水稻病害检测算法。首先，针对YOLOv7中原上采样模块在水稻病害语义信息提取方面的不足，引入NC(Nearest CARAFE)上采样模块，显著提升网络模型在恢复水稻叶片图像细节方面的能力，使模型能更准确地捕捉和识别病害特征。其次，为进一步加强模型的特征提取和融合能力，提出DFPN结构，以改进原模型的颈部设计。最后，采用Mixup和Mosaic技术对原始数据集进行增强处理，以增强模型的泛化能力和鲁棒性。结果表明，DNC-YOLOv7算法在数据集上的平均检测精度从原始的83.4%显著提升至93.2%，相较传统的YOLOv7算法，平均检测精度提高9.8%。

关键词: 水稻叶片, 病害检测, YOLOv7, 数据增强, CARAFE