Lin Jianwu, Zhang Xin, Qin Yongbin, Yang Shengxian, Wen Xingtian, Cernava Tomislav, Migheli Quirico, Chen Xiaoyulong
Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
Plant Phenomics. 2024 Jul 31;6:0208. doi: 10.34133/plantphenomics.0208. eCollection 2024.
Accurate identification of plant diseases is important for ensuring the safety of agricultural production. Convolutional neural networks (CNNs) and visual transformers (VTs) can extract effective representations of images and have been widely used for the intelligent recognition of plant disease images. However, CNNs have excellent local perception with poor global perception, and VTs have excellent global perception with poor local perception. This makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition tasks. In this paper, we propose a local and global feature-aware dual-branch network, named LGNet, for the identification of plant diseases. More specifically, we first design a dual-branch structure based on CNNs and VTs to extract the local and global features. Then, an adaptive feature fusion (AFF) module is designed to fuse the local and global features, thus driving the model to dynamically perceive the weights of different features. Finally, we design a hierarchical mixed-scale unit-guided feature fusion (HMUFF) module to mine the key information in the features at different levels and fuse the differentiated information among them, thereby enhancing the model's multiscale perception capability. Subsequently, extensive experiments were conducted on the AI Challenger 2018 dataset and the self-collected corn disease (SCD) dataset. The experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the AI Challenger 2018 dataset and the SCD dataset, with accuracies of 88.74% and 99.08%, respectively.
准确识别植物病害对于确保农业生产安全至关重要。卷积神经网络（CNN）和视觉Transformer（VT）能够提取图像的有效特征表示，并已广泛应用于植物病害图像的智能识别。然而，CNN具有出色的局部感知能力但全局感知能力较差，而VT具有出色的全局感知能力但局部感知能力较差。这使得难以进一步提高CNN和VT在植物病害识别任务上的性能。在本文中，我们提出了一种用于识别植物病害的局部和全局特征感知双分支网络，名为LGNet。具体而言，我们首先基于CNN和VT设计了一种双分支结构来提取局部和全局特征。然后，设计了一个自适应特征融合（AFF）模块来融合局部和全局特征，从而驱动模型动态感知不同特征的权重。最后，我们设计了一个分层混合尺度单元引导的特征融合（HMUFF）模块来挖掘不同层次特征中的关键信息并融合其中的差异化信息，从而增强模型的多尺度感知能力。随后，在AI Challenger 2018数据集和自行收集的玉米病害（SCD）数据集上进行了广泛的实验。实验结果表明，我们提出的LGNet在AI Challenger 2018数据集和SCD数据集上均取得了领先的识别性能，准确率分别为88.74%和99.08%。
