天津农学院学报 ›› 2024, Vol. 31 ›› Issue (3): 78-84.doi: 10.19640/j.cnki.jtau.2024.03.014

• 研究与简报 • 上一篇    下一篇

基于Vue+Springboot的中草药病害识别管理系统研究

周晓睿1, 杨磊1,通信作者, 宋欣1, 李冰2, 张坤辑1, 欧阳凯1   

  1. 1.天津农学院 工程技术学院,天津 300392;
    2.恒银金融科技股份有限公司,天津 300308
  • 收稿日期:2023-08-04 出版日期:2024-06-30 发布日期:2024-07-15
  • 通讯作者: 杨磊(1980—),女,副教授,硕士,主要从事智能农机装备方面的研究。E-mail:yanglei2016@tjau.edu.cn。
  • 作者简介:周晓睿(1999—),女,硕士在读,主要从事软件开发和深度学习方面的研究。E-mail:zhouxiaorui6881@163.com。
  • 基金资助:
    京津冀协同创新共同体建设专项(22347402D)

Research on Chinese herbal disease identification management system based on Vue+Springboot

Zhou Xiaorui1, Yang Lei1,Corresponding Author, Song Xin1, Li Bing2, Zhang Kunji1, Ouyang Kai1   

  1. 1. College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China;
    2. Cashway Fintech Co., Ltd., Tianjin 300308, China
  • Received:2023-08-04 Online:2024-06-30 Published:2024-07-15

摘要: 中医药在当今医学领域具有举足轻重的地位,中草药的需求量也逐年提升,中草药病害是造成中草药减产的重要原因之一。目前仍然使用人工识别中草药病害的方法,主观性强、准确率较低,为了更加科学高精度识别中草药病害种类,本文开发了一个中草药病害识别管理系统。系统采用前后端分离模式,前端使用Vue和Element-Ui框架,后端使用Springboot和Mybatis技术框架,数据库采用MySQL,并融合深度学习预测模型科学识别中草药病害种类,并在现有的识别系统基础上对深度学习YOLOv5模型进行改进,使识别准确率由原来的95.3%提升到98.2%。系统中增加了中草药病害识别功能和监测中草药生长环境数据展示,具有监测中草药生长环境和高精度识别病害种类的功能,该系统具有很强的实用性和服务性,能够减轻种植人员的劳动力,增加经济收益,同时推动智慧农业的发展。

关键词: Vue框架, Springboot框架, MySQL数据库, 中草药病害, 深度学习

Abstract: Chinese medicine plays an important role in today’s medical field, and the demand for Chinese herbal medicine is also increasing year by year. However, Chinese herbal medicine disease is one of the important reasons for the reduction of its production. At present, the method of artificial identification of Chinese herbal diseases is still used, which is subjective and has low accuracy. In order to identify Chinese herbal diseases more scientifically and accurately, this paper develops a Chinese herbal disease identification management system. The Chinese herbal medicine disease identification and management system adopts the front-back separation mode, the front-end uses Vue and Element-Ui framework, the back-end uses Springboot and Mybatis technical framework, the database uses MySQL, and the deep learning prediction model is integrated to scientifically identify the types of Chinese herbal medicine diseases. In this paper, the deep learning YOLOv5 model is improved on the basis of the existing recognition system, and the recognition accuracy is increased from the original 95.3% to 98.2%. The system has added the function of Chinese herbal medicine disease identification and monitoring of Chinese herbal medicine growth environment data display, which has the effect of monitoring the growth environment of Chinese herbal medicine and high-precision identification of disease types. The system has a strong practicality and service, can reduce the labor force of growers, increase economic benefits, and promote the development of smart agriculture.

Key words: Vue framework, Springboot framework, MySQL database, Chinese herbal diseases, deep learning

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