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Abstract：
The identification of plant leaf diseases is crucial in precision agriculture, playing a pivotal role in advancing the modernization of agriculture. Timely detection and diagnosis of leaf diseases for preventive measures significantly contribute to enhancing both the quantity and quality of agricultural products, thereby fostering the in-depth development of precision agriculture. However, despite the rapid development of research on plant leaf disease identification, it still faces challenges such as insufficient agricultural datasets and the problem of deep learning-based disease identification models having numerous training parameters and insufficient accuracy. This paper proposes a plant leaf disease identification method based on improved SinGAN and improved ResNet34 to address the aforementioned issues. Firstly, an improved SinGAN called Reconstruction-Based Single Image Generation Network (ReSinGN) is proposed for image enhancement. This network accelerates model training speed by using an autoencoder to replace the GAN in the SinGAN and incorporates a Convolutional Block Attention Module (CBAM) into the autoencoder to more accurately capture important features and structural information in the images. Random pixel Shuffling are introduced in ReSinGN to enable the model to learn richer data representations, further enhancing the quality of generated images. Secondly, an improved ResNet34 is proposed for plant leaf disease identification. This involves adding CBAM modules to the ResNet34 to alleviate the limitations of parameter sharing, replacing the ReLU activation function with LeakyReLU activation function to address the problem of neuron death, and utilizing transfer learning-based training methods to accelerate network training speed. This paper takes tomato leaf diseases as the experimental subject, and the experimental results demonstrate that: (1) ReSinGN generates high-quality images at least 44.6 times faster in training speed compared to SinGAN. (2) The Tenengrad score of images generated by the ReSinGN model is 67.3, which is improved by 30.2 compared to the SinGAN, resulting in clearer images. (3) ReSinGN model with random pixel Shuffling outperforms SinGAN in both image clarity and distortion, achieving the optimal balance between image clarity and distortion. (4) The improved ResNet34 achieved an average recognition accuracy, recognition precision, recognition accuracy (redundant as it\'s similar to precision), recall, and F1 score of 98.57, 96.57, 98.68, 97.7, and 98.17%, respectively, for tomato leaf disease identification. Compared to the original ResNet34, this represents enhancements of 3.65, 4.66, 0.88, 4.1, and 2.47%, respectively.
摘要：
植物叶部病害的识别在精准农业中至关重要,在推进农业现代化中发挥着举足轻重的作用。及时检测和诊断叶部病害的预防措施大大有助于提高农产品的数量和质量，从而促进精准农业的深入发展。然而,尽管植物叶部病害鉴定研究发展迅速,它仍然面临挑战，例如农业数据集不足以及基于深度学习的疾病识别模型具有大量训练参数和准确性不足的问题。针对上述问题,提出了一种基于改进SinGAN和改进ResNet34的植物叶部病害识别方法。首先,提出了一种改进的SinGAN，称为基于重建的单幅图像生成网络（ReSinGN），用于图像增强。该网络通过使用自动编码器代替SinGAN中的GAN来加快模型训练速度，并将卷积块注意模块（CBAM）集成到自动编码器中，以更准确地捕获图像中的重要特征和结构信息。ReSinGN中引入了随机像素改组，以使模型能够学习更丰富的数据表示，进一步增强生成图像的质量。其次,提出了一种改进的ResNet34用于植物叶片病害识别。这涉及将CBAM模块添加到ResNet34，以减轻参数共享的限制，用LeakyReLU激活函数代替ReLU激活函数来解决神经元死亡的问题，利用基于迁移学习的训练方法加快网络训练速度。本文以番茄叶部病害为实验对象,实验结果表明：（1）与SinGAN相比，ReSinGN生成的高质量图像的训练速度至少快44.6倍。（2）ReSinGN模型生成的图像的Tengrade得分为67.3，与SinGAN相比提高了30.2，产生更清晰的图像。(3)具有随机像素混洗的ReSinGN模型在图像清晰度和失真方面都优于SinGAN,实现图像清晰度和失真之间的最佳平衡。(4)改进的ResNet34实现了平均识别精度,识别精度,识别精度(冗余，因为它类似于精度)，召回，F1得分为98.57、96.57、98.68、97.7和98.17％，分别,用于番茄叶部病害鉴定。与原始ResNet34相比，这代表了3.65、4.66、0.88、4.1和2.47％的增强，分别。
