【摘要】 基于农业技术与信息化技术的不断发展与融合，针对当前河北省农作物害虫识别准确率和效率低等问题，提出了一种基于Asp.NET Core MVC架构的残差神经网络害虫图像识别系统。该系统首先通过移动采集终端和网络图片爬虫收集目标分类图片信息，再使用数据增强技术扩充样本库，得到神经网络训练模型的数据集；然后通过搭建机器学习框架，分别引入ResNet-50、ResNet-101、ResNet-152残差网络模型，对数据集执行训练并验证其准确度；最后将准确度最高的训练结果模型运用至农作物害虫分类服务系统。经验证，该识别模型具有良好的适用性和鲁棒性，可为河北省主要农作物虫害提供识别及诊断功能。更多还原

【Abstract】 Based on the continuous development and integration of agricultural technology and information technology,residual neural network pest image recognition system on Asp.NET Core MVC architecture was proposed to solve the low accuracy and efficiency of crop pest recognition in Hebei province. Firstly,the system collects the image information of target classification by mobile acquisition terminal and network image crawler,and uses data enhancement technology to expand the sample library to obtain the data set of neural network training model. Then by building machine learning framework,ResNet-50,ResNet-101,ResNet-152 residual network models are introduced to train the data set and verify its accuracy. Finally,the most accurate training results model is applied to crop pest classification service system. The identification model has good applicability and robustness,which can provide identification and diagnosis functions for pests of main crops in Hebei province.更多还原