河南农业科学 ›› 2020, Vol. 49 ›› Issue (12): 159-163.DOI: 10.15933/j.cnki.1004-3268.2020.12.023

所属专题: 作物图像采集与识别专题

• 农业信息与工程·农产品加工 • 上一篇    下一篇

基于Python爬虫和特征匹配的水稻病害图像智能采集

杨天乐1,2,钱寅森1,2,武威1,2,孙成明1,2,刘涛1,2   

  1. (1.江苏省作物遗传生理国家重点实验室/江苏省作物栽培生理重点实验室/扬州大学 农学院,江苏 扬州 225009;2.江苏省粮食作物现代产业技术协同创新中心/扬州大学,江苏 扬州 225009)
  • 收稿日期:2020-02-15 出版日期:2020-12-15 发布日期:2020-12-15
  • 通讯作者: 刘涛(1987-),男,江苏徐州人,讲师,博士,主要从事智能农业、作物图像分析、无人机遥感研究。E-mail:tliu@yzu.edu.com孙成明(1973-),男,江苏宿迁人,教授,博士,主要从事作物精准栽培与农业信息技术研究。E-mail:cmsun@yzu.edu.com
  • 作者简介:杨天乐(1994-),男,江苏徐州人,在读博士研究生,研究方向:农业信息技术和作物栽培。E-mail:tianley21@qq.com
  • 基金资助:
    国家自然科学基金项目(31701355,31671615);国家博士后基金项目(2016M600448,2018T110560);国家重点研发计划项目(2016YFD0300107);2017年江苏省优势学科项目

Intelligent Acquisition of Rice Disease Images Based on Python Crawler and Feature Matching

YANG Tianle1,2,QIAN Yinsen1,2,WU Wei1,2,SUN Chengming1,2,LIU Tao1,2   

  1. (1.Agricultural College of Yangzhou University/Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology,Yangzhou 225009,China; 2.Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crop/Yangzhou University,Yangzhou 225009,China)
  • Received:2020-02-15 Published:2020-12-15 Online:2020-12-15

摘要: 为及时诊断和防治水稻病害,通过计算机技术和图像处理技术进行病害诊断。利用Python爬虫技术编写基于水稻病害关键词的图像爬虫程序,在此基础上使用Matlab图像处理模块的特征匹配对图像集进行筛选,提高图像采集的准确度。结果表明,只利用Python爬虫技术获取的水稻病害图像,除胡麻叶斑病外,提取的准确率均高于50.00%,其中赤霉病提取效果最好,准确率达到72.7%。而通过特征匹配筛选后图像错检率在6.00%以下,不仅提高了数据采集的精度,也表明水稻病害图像智能采集方法可行。

关键词: 水稻病害, Python, 特征匹配, 图像处理, 农业信息

Abstract: For timely diagnose and prevent the rice diseases,computer technology and image processing technology were used for disesae diagnosis. Python crawler technology was used to compile image crawler programs based on rice disease keywords.The feature matching of Matlab image was used to filter the image set to improve the accuracy of image collection.The results showed that the extraction accuracy of rice disease images obtained only by Python crawler technology was higher than 50. 00%,except bipolaris oryzae.Among them,the extraction effect of gibberellic disease was the best,with an accuracy rate of 72.7%.The false detection rate of images after the feature matching screening was below 6.00%,which not only improved the accuracy of data collection,but also showed that rice diseases image acquisition through the intelligent method was feasible.

Key words: Rice disease, Python, Feature matching, Image processing, Agricultural information

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