Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life.

中文翻译：

---

DS-MENet 用于柑橘病害的分类。

受各种环境因素的影响，柑橘在生长过程中会频繁出现病害，给农业发展带来巨大障碍。本文提出了一种识别和分类柑橘病害的新方法。首先，本文设计了一种基于MSRCR算法和拉普拉斯优化的同态滤波算法（HFLF-MS）的图像增强方法，以突出柑橘的病害特征。其次，我们设计了一个基于 DenseNet-121 骨干结构的新型神经网络 DS-MENet。在 DS-MENet 中，将 Dense Block 中的常规卷积替换为 depthwise separable convolution，从而减少了网络参数。ReMish激活函数用于缓解ReLU函数引起的神经元死亡问题，提高模型的鲁棒性。为了进一步提高对柑橘病害信息的关注度和特征信息提取能力，本工作设计了一种多通道融合主干增强方法（MCF）来处理密集块。我们使用 10 折交叉验证方法进行实验。DS-MENet在加入噪声后的数据集上的平均分类准确率可以达到95.02%。这表明该方法具有良好的性能，对现实生活中柑橘病害的分类具有一定的可行性。DS-MENet在加入噪声后的数据集上的平均分类准确率可以达到95.02%。这表明该方法具有良好的性能，对现实生活中柑橘病害的分类具有一定的可行性。DS-MENet在加入噪声后的数据集上的平均分类准确率可以达到95.02%。这表明该方法具有良好的性能，对现实生活中柑橘病害的分类具有一定的可行性。