Raman spectroscopy can be used for accurately detecting pesticides and determining the chemical composition of a pesticide. To facilitate field detection, the present study used a portable Raman spectrometer for analysis. However, this spectrometer was found to be susceptible to noise interference and signal offsets, which increased the difficulty of pesticide identification. The most commonly used algorithm for Raman spectrum identification is principal component analysis (PCA). However, accurate classification often cannot be achieved with PCA because of the offset and noise in the Raman spectrum data. Therefore, in this study, after the collected Raman spectrum data were processed using the small-step, center-weighted moving-average method, these data were employed to train a convolutional neural network (CNN) model for prediction. To optimize the CNN model, the hyperparameters of the CNN were adjusted using various optimization algorithms, and the optimal solution was obtained after multiple iterations. Data preprocessing and architecture training models were then constructed in a self-optimized manner to improve the ability of the algorithm model to handle diverse types of data. Finally, a CNN model optimized using the cat swarm optimization algorithm was developed. This model was trained on 3000 samples containing three pesticides, and its accuracy for pesticide composition identification was discovered to be 89.33%.

拉曼光谱可用于准确检测农药并确定农药的化学成分。为了便于现场检测，本研究使用便携式拉曼光谱仪进行分析。然而，该光谱仪被发现容易受到噪声干扰和信号偏移，这增加了农药识别的难度。拉曼光谱识别最常用的算法是主成分分析（PCA）。然而，由于拉曼光谱数据中的偏移和噪声，PCA通常无法实现准确的分类。因此，在本研究中，在使用小步长中心加权移动平均法对收集的拉曼光谱数据进行处理后，使用这些数据来训练卷积神经网络（CNN）模型进行预测。为了优化CNN模型，利用各​​种优化算法调整CNN的超参数，经过多次迭代得到最优解。然后以自我优化的方式构建数据预处理和架构训练模型，以提高算法模型处理不同类型数据的能力。最后，开发了使用猫群优化算法优化的CNN模型。该模型对 3000 个含有 3 种农药的样品进行训练，发现其农药成分识别准确率为 89.33%。