基于云服务对田间杂草智能检测系统的研究

Research on Field Weed Intelligent Detection System Based on Cloud Service

为了解决田间复杂的环境使传统图像处理对杂草识别精度差的问题,该研究对8种常见的杂草进行采集,数据集由17509张带有标签的图像组成,采用迁移学习的方式对田间杂草进行识别,并对训练出的模型进行微调,使其进一步提高识别的准确率。对VGG 19、Inception V4、ResNesXt 101和NASNet-mobile 4种模型进行比对,选用模型参数小且准确率高的NASNet-mobile模型,并将其部署到云服务中。云服务端使用Gin搭建模型交互,用于识别杂草并返回识别信息;使用CSS和Java script语言及Element封装的组件开发前端服务,用于实现数据的采集、上传与信息反馈。NASNet-mobile模型在部署的服务器中的性能达到了每幅图像的平均时间为285 ms,对8种杂草准确率达到91.43%,对于扁轴木与飞机草识别率达到98%,可为田间杂草信息检测和调查提供技术支持。

In order to solve the problem of the poor accuracy of weed recognition by traditional image processing in the complex environment of the field,this study collected 8 kinds of common weeds.The data set was composed of 17 509 labeled images.Weeds were identified and the trained model is fine-tuned to further improve the accuracy of the recognition.By comparing the four models of VGG,Inception,ResNeXt and NASNet,the NASNet-mobile model with small model parameters and high accuracy was selected and deployed to the cloud service.The cloud server used Gin to build model interactions for identifying weeds and returning identification information.It used CSS and Javascript language and components encapsulated by Element to develop front-end services for data collection,upload,and information feedback.The performance of NASNet-mobile model in the deployed server was 285 ms per image,the accuracy of 8 weeds was 91.43%,and the recognition rate of flat axis wood and plane grass was 98%,which could provide technical support for weed information detection and investigation in the field.