目的 随着人工智能和大数据技术的不断发展，针对常规玉米虫害识别方法存在的准确率和效率低等问题，本文提出了一种基于改进GoogLeNet卷积神经网络模型的玉米螟虫害图像识别方法。方法 首先通过迁移学习将GoogLeNet的Inception-v4网络结构知识转移到玉米螟Pyrausta nubilalis虫害识别的任务上，构建模型的训练方式；然后通过数据增强技术对玉米螟虫图像进行样本扩充，得到神经网络训练模型的数据集；同时利用Inception模块拥有多尺度卷积核提取多尺度玉米螟虫害分布特征的能力构建网络模型，并在试验过程中对激活函数、梯度下降算法等模型参数进行优化；最后引入批标准化（BN）操作加速优化模型网络训练，并将该模型运用到玉米螟虫害识别中。结果 基于TensorFlow框架下的试验结果表明，优化后的神经网络算法对玉米螟虫害图像平均识别准确率达到了96.44%。结论 基于优化的卷积神经网络识别模型具有更强的鲁棒性和适用性，可为玉米等农作物虫害识别、智能诊断提供参考。
Objective With the continuous development of artificial intelligence and big data technology, aiming at solving the problems of low accuracy and low efficiency in conventional identification methods of corn pest, we proposed a corn borer image identification method based on the improved GoogLeNet convolution-neural network model.Method Firstly, through migration learning, the structural knowledge of the Inception-v4 network of GoogLeNet was transferred to the task of corn borer (Pyrausta nubilalis) identification, and the training mode of model construction was established. The data set of neural network training model was obtained by expanding the sample of corn borer image through data enhancement technique. At the same time, the Inception module was used to construct the network model with the ability of multi-scale convolution kernel extraction of the distribution characteristics of multi-scale corn borer, and the activation function, gradient descent algorithm and other model parameters were optimized in the experimental process. Finally, batch normalization (BN) operation was performed to accelerate optimizating model network training, and the model was applied in corn borer identification.Result Experimental results of TensorFlow framework showed that the average recognition accuracy of the optimized neural network algorithm for corn borer image was 96.44%.Conclusion The convolutional neural network recognition model based on optimization has higher robustness and feasibility, which can provide a reference for identification and intelligent diagnosis of plant pests on corn and other crops.