基于数字图像棉花黄萎病诊断与防治的管理远程监测

doi:10.6048/j.issn.1001-4330.2020.06.022

新疆农业科学 ›› 2020, Vol. 57 ›› Issue (6): 1166-1174.DOI: 10.6048/j.issn.1001-4330.2020.06.022

• • 上一篇

基于数字图像棉花黄萎病诊断与防治的管理远程监测

石志钰, 裴雅琨, 朱玉涛, 贾玉姣, 胡晓倩, 侯士聪, 侯玉霞

中国农业大学理学院,北京 100193

收稿日期:2020-01-20 发布日期:2020-05-11
通信作者: 侯玉霞(1963-),女,教授,研究方向为棉花病害监测与防治,(E-mail) yuxiacau@163.com
作者简介:石志钰(1988-),男,硕士,研究方向为信息工程与数字农业,(E-mail) 369185018@qq.com
基金资助:
国家重点研发计划(2017YFD0201900)

Study on Remote Monitoring of Cotton Verticillium wilt Diagnosis and Control Management Based on Digital Image

SHI Zhiyu, PEI Yakun, ZHU Yutao, JIA Yujiao, HU Xiaoqian, HOU Shicong, HOU Yuxia

College of Science, China Agricultural University, Beijing 100193, China

Received:2020-01-20 Published:2020-05-11
Correspondence author: HOU Yuxia (1963- ),female,professor,research direction for the monitoring and control of cotton diseases, (E-mail)yuxiacau@163.com
Supported by:
Supported by the National Key R & D Program Project (2017YFD0201900)

摘要/Abstract

摘要： 【目的】获得病害的典型病害植被指数特征及其病害防治管理,为诊断和防治棉花黄萎病提供有效工具,为病害监测模型和防治管理提供决策支撑。研究冠层图像RGB至HIS空间的变换与典型病害植被指数的关系,通过Relief-F算法获得棉花黄萎病症状变化敏感的特征,构建病害监测模型。【方法】采集与结构、叶绿素相关的植被指数与棉花黄萎病症状特征的数字图像,与病害植被指数和病害严重度等级的识别有较好的对数关系。获得病害症状特征变化的典型谱段,构建棉花黄萎病病情指数,建立病害病情监测模型。【结果】获得与棉花叶片结构、叶绿素相关的植被指数和棉花黄萎病症状特征的数字图像,与不同严重度病害植被指数和病害严重度等级的诊断具有对数关系。提取棉花黄萎病症状特征变化的典型谱段,构建病害监测模型,建立防控棉花黄萎病数据的安全架构。【结论】实现获取棉花黄萎病病害的发病情况的监测和病害防控管理,为棉花黄萎病大面积精准监测和防控提供新的思路。

关键词: 数字图像; 棉花黄萎病; 病害植被指数; 病害监测; 防控管理

Abstract: 【Objective】 In order to accurately monitor and control cotton Verticillium wilt, to provide applicable tools, to obtain the characteristics of typical Verticillium wilt symptom vegetation index and the management of disease control, to provide decision support for the monitoring model and control management of cotton Verticillium wilt.【Method】The relationship between the transformation of vegetation canopy image RGB to HIS space and the vegetation index of typical diseases was studied. The sensitive characteristics of Verticillium wilt symptom changes were obtained by relief-f algorithm, the Verticillium wilt was monitored by SVM, and the monitoring model of Verticillium wilt was established by SVM optimized by GA genetic algorithm.【Results】To obtain a digital image of vegetation index and symptom characteristics of Verticillium wilt of cotton related to leaf structure and chlorophyll content. There was a logarithmic relationship with the diagnosis of vegetation index and severity grade of disease with different severity. The characteristic spectrum of cotton Verticillium wilt was obtained, the disease monitoring model was constructed, and the safety framework for controlling cotton Verticillium wilt was established. 【Conclusion】To achieve the monitoring and disease control management of Cotton Verticillium wilt, and provide new ideas for large-scale accurate monitoring and control of Cotton Verticillium wilt.

Key words: Digital image; cotton Verticillium wilt; disease vegetation index; disease monitoring; control management

中图分类号:

S435.61

石志钰, 裴雅琨, 朱玉涛, 贾玉姣, 胡晓倩, 侯士聪, 侯玉霞. 基于数字图像棉花黄萎病诊断与防治的管理远程监测[J]. 新疆农业科学, 2020, 57(6): 1166-1174.

SHI Zhiyu, PEI Yakun, ZHU Yutao, JIA Yujiao, HU Xiaoqian, HOU Shicong, HOU Yuxia. Study on Remote Monitoring of Cotton Verticillium wilt Diagnosis and Control Management Based on Digital Image[J]. Xinjiang Agricultural Sciences, 2020, 57(6): 1166-1174.

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基于数字图像棉花黄萎病诊断与防治的管理远程监测

Study on Remote Monitoring of Cotton Verticillium wilt Diagnosis and Control Management Based on Digital Image

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