Zhang Ning, Zhang Enxu, Qi Guowei, Li Fei, Lv Cheng
Engineering Research Center of Hydrogen Energy Equipment & Safety Detection, Universities of Shaanxi Province, Xijing University, Xi'an, 710123, China.
Sci Rep. 2025 Aug 7;15(1):28974. doi: 10.1038/s41598-025-13689-7.
Grape disease image recognition is an important part of agricultural disease detection. Accurately identifying diseases allows for timely prevention and control at an early stage, which plays a crucial role in reducing yield losses. This study addresses the problems in grape leaf disease recognition under small-sample conditions, such as the difficulty in capturing multi-scale features, the minuteness of features, and the weak adaptability of traditional data augmentation methods. It proposes a solution that combines a multi-scale feature hybrid fusion architecture with data augmentation. The innovation of this study lies in the following four dimensions: (1) Utilize generative models to enhance the cross-category data balancing ability under small-sample conditions and enrich the sample information in the dataset. (2) Innovatively propose the LVT Block, a multi-scale information perception hybrid module based on the Ghost and Transformer structures. This module can effectively acquire and fuse multi-scale information and global information in the feature map. (3) Use the dense connection method to combine the LVT Block and the MARI Block to propose a new architecture, the DLVT Block. By fusing multi-scale information and global information, it improves the richness of feature information. It also uses the MARI to enhance the model's perception of disease areas and constructs an end-to-end lightweight model, DLVTNet, using the DLVT Block. Experiments show that this method achieves an average recognition rate of 98.48% on the New Plant Diseases Dataset. The number of parameters is reduced to 42.7% of that of MobileNetV4, and it maintains an accuracy of 96.12% in the tomato leaf disease test. This paper embeds pathological features into the generative adversarial process, which can effectively alleviate the problem of insufficient samples in intelligent agricultural detection. It provides a new method system with strong interpretability and excellent generalization performance for disease detection.
葡萄病害图像识别是农业病害检测的重要组成部分。准确识别病害能够在早期及时进行预防和控制，这对减少产量损失起着至关重要的作用。本研究针对小样本条件下葡萄叶部病害识别存在的问题，如多尺度特征捕捉困难、特征细微以及传统数据增强方法适应性弱等。提出了一种将多尺度特征混合融合架构与数据增强相结合的解决方案。本研究的创新点体现在以下四个方面：（1）利用生成模型增强小样本条件下的跨类别数据平衡能力，丰富数据集中的样本信息。（2）创新性地提出基于Ghost和Transformer结构的多尺度信息感知混合模块LVT Block。该模块能够有效获取并融合特征图中的多尺度信息和全局信息。（3）采用密集连接方法将LVT Block和MARI Block相结合，提出新架构DLVT Block。通过融合多尺度信息和全局信息，提高了特征信息的丰富度。还利用MARI增强模型对病害区域的感知，并使用DLVT Block构建了端到端的轻量级模型DLVTNet。实验表明，该方法在新植物病害数据集上的平均识别率达到98.48%。参数数量减少至MobileNetV4的42.7%，在番茄叶部病害测试中保持了96.12%的准确率。本文将病理特征嵌入生成对抗过程，能够有效缓解智能农业检测中样本不足的问题。为病害检测提供了一种具有强解释性和优异泛化性能的新方法体系。
