Abstract: In view of the difficulties of farmers and grassroots plant protection personnel in identifying crop diseases, the identification model is established by using VGG16 and Resnet50 for 18 crop diseases of apple, corn, grape and tomato as the research object. Through data pretreatment, data enhancement, model parameter optimization and model cross validation, the single crop multidisease identification and multi crop multidisease identification models are constructed. The performance comparison results show that VGG16 has better recognition performance than Resnet50, and the recognition accuracy of VGG16 model is more than 96%. After analyzing the VGG16 recognition model, it is found that the recognition performance of the single crop multidisease identification model has the best recognition performance. Therefore, based on the method of establishing single crop multidisease identification model, combined with smart phone, Web technology and network programming technology, this paper is proposed to develop an intelligent identification system of crop diseases. The system can provide users with accurate identification results, disease knowledge and prevention methods. The Socket network service of the system can be used as an independent module to provide a unified interface for crop disease identification for agricultural robots, intelligent agricultural machinery, unmanned aerial vehicle, agricultural expert systems, and so on. This study can provide technical support for the informatization and intellectualization of agricultural plant protection.

Key words: deep convolutional neural network, recognition of crop disease, smart phone, intelligent recognition system, Socket network

摘要： 针对广大农户和基层植保人员对作物病害识别困难的问题，以苹果、玉米、葡萄和番茄4种作物18种病害为研究对象，采用VGG16和Resnet50建立识别模型。通过数据预处理、数据增强、模型参数优化、模型交叉验证等，构建单作物多病害识别和多作物多病害识别模型。性能对比结果表明VGG16识别性能优于Resnet50，VGG16模型的识别正确率都达到96%以上。对VGG16识别模型分析后发现根据作物发病特点建立的单作物多病害识别模型性能更好。因此，本文提出分类建立单作物多病害识别模型的方法，结合智能手机、Web技术和网络编程技术，研发一个农作物病害智能识别系统。本系统可为用户提供精确的识别结果、病害知识和防治方法。系统的Socket网络服务可作为独立模块，为农业机器人、智能农机、无人机、农业专家系统等提供统一的作物病害识别接口。本研究可为农业植保信息化和智能化提供技术支撑。

关键词: 深度卷积神经网络, 病害识别, 智能手机, 智能识别系统, Socket网络