Zhang Enxu, Zhang Ning, Li Fei, Lv Cheng
Engineering Research Center of Hydrogen Energy Equipment& Safety Detection, Universities of Shaanxi Province, Xijing University, Xi'an, China.
Front Plant Sci. 2024 Aug 6;15:1420584. doi: 10.3389/fpls.2024.1420584. eCollection 2024.
Tomato disease image recognition plays a crucial role in agricultural production. Today, while machine vision methods based on deep learning have achieved some success in disease recognition, they still face several challenges. These include issues such as imbalanced datasets, unclear disease features, small inter-class differences, and large intra-class variations. To address these challenges, this paper proposes a method for classifying and recognizing tomato leaf diseases based on machine vision. First, to enhance the disease feature details in images, a piecewise linear transformation method is used for image enhancement, and oversampling is employed to expand the dataset, compensating for the imbalanced dataset. Next, this paper introduces a convolutional block with a dual attention mechanism called DAC Block, which is used to construct a lightweight model named LDAMNet. The DAC Block innovatively uses Hybrid Channel Attention (HCA) and Coordinate Attention (CSA) to process channel information and spatial information of input images respectively, enhancing the model's feature extraction capabilities. Additionally, this paper proposes a Robust Cross-Entropy (RCE) loss function that is robust to noisy labels, aimed at reducing the impact of noisy labels on the LDAMNet model during training. Experimental results show that this method achieves an average recognition accuracy of 98.71% on the tomato disease dataset, effectively retaining disease information in images and capturing disease areas. Furthermore, the method also demonstrates strong recognition capabilities on rice crop disease datasets, indicating good generalization performance and the ability to function effectively in disease recognition across different crops. The research findings of this paper provide new ideas and methods for the field of crop disease recognition. However, future research needs to further optimize the model's structure and computational efficiency, and validate its application effects in more practical scenarios.
番茄病害图像识别在农业生产中起着至关重要的作用。如今，虽然基于深度学习的机器视觉方法在病害识别方面取得了一些成功，但它们仍然面临若干挑战。这些挑战包括数据集不平衡、病害特征不清晰、类间差异小以及类内变化大等问题。为应对这些挑战，本文提出了一种基于机器视觉的番茄叶部病害分类识别方法。首先，为增强图像中的病害特征细节，采用分段线性变换方法进行图像增强，并采用过采样来扩充数据集，以弥补数据集的不平衡。接下来，本文引入了一种具有双重注意力机制的卷积块，称为DAC块，用于构建一个名为LDAMNet的轻量级模型。DAC块创新性地分别使用混合通道注意力（HCA）和坐标注意力（CSA）来处理输入图像的通道信息和空间信息，增强了模型的特征提取能力。此外，本文还提出了一种对噪声标签具有鲁棒性的稳健交叉熵（RCE）损失函数，旨在减少训练过程中噪声标签对LDAMNet模型的影响。实验结果表明，该方法在番茄病害数据集上的平均识别准确率达到98.71%，有效地保留了图像中的病害信息并捕捉到病害区域。此外，该方法在水稻作物病害数据集上也表现出强大的识别能力，表明具有良好的泛化性能以及在不同作物病害识别中有效发挥作用的能力。本文的研究结果为作物病害识别领域提供了新的思路和方法。然而，未来的研究需要进一步优化模型的结构和计算效率，并在更多实际场景中验证其应用效果。
