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| 综合遥感与气象信息的小麦白粉病监测方法 | |
| 引用本文： | 聂臣巍,袁 琳,王保通,金秀良,黄文江,张竞成,杨贵军.综合遥感与气象信息的小麦白粉病监测方法[J].植物病理学报,2016,46(2):285-288. |
| 作者姓名： | 聂臣巍 袁 琳 王保通 金秀良 黄文江 张竞成 杨贵军 |
| 作者单位： | 北京农业信息技术研究中心,北京,100097; 旱区作物逆境生物学国家重点实验室/西北农林科技大学,杨凌 712100; 中国科学院遥感与数字地球研究所,数字地球重点实验室,北京 100094 |
| 基金项目： | 北京科技新星计划(Z151100000315059);小麦条锈病遥感监测中的农田胁迫易混机理与区分模型研究(41301476);禾谷类白粉病和赤霉病综合治理技术研究与示范(201303016) |
| 摘 要： | <正>小麦白粉病是小麦生产过程中的主要病害之一,常在小麦生育后期爆发,造成严重的减产和品质降低。对该病的准确监测是植保工作的一个重点,对病害的有效防治具有重要意义~(1,2])。近年来,遥感数据的空间连续观察能力及其包含的丰富信息引起作物病害监测领域的广泛关 |
| 收稿时间： | 2015-01-03 |
Monitoring wheat powdery mildew based on integrated remote sensing and meteo-rological information |
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| NIE Chen-wei,YUAN Lin,WANG Bao-tong,JIN Xiu-liang,HUANG Wen-jiang,ZHANG Jing-cheng,YANG Gui-jun.Monitoring wheat powdery mildew based on integrated remote sensing and meteo-rological information[J].Acta Phytopathologica Sinica,2016,46(2):285-288. | |
| Authors: | NIE Chen-wei YUAN Lin WANG Bao-tong JIN Xiu-liang HUANG Wen-jiang ZHANG Jing-cheng YANG Gui-jun |
| Institution: | Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; State Key Laboratory of Crop Stress Biology for Arid Areas/College of Plant Protection, Northwest A&F University, Yangling 712100, China;Key laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China |
| Abstract: | It should be taken seriously to monitor powdery mildew (PM) of wheat quickly and accurately in continuous space since the outbreak of this disease may cause substantially reduction of output and lower the quality of wheat. A new method was presented to monitor the external environment of the PM pathogen based on the remote sensing information including greenness wetness and land surface temperature (LST) and the meteorological information including temperature and the number of rainy days. Three kinds of models were established using algorithms Fisher liner discriminant analysis (FLDA) and support vector machine (SVM), respectively. And these models were individually used to supervise the incidence of PM in the western regions of Guanzhong Plain, Shaanxi Province. The results showed that the integrating models (integrating the remote sensing information and the meteorological information) reached at a satisfactory accuracy (FLDA: 74%, SVM: 77%). The integrating models performed better than the meteorological models (FLDA: 60%, SVM: 65%) and the remote sensing models (FLDA: 65%, SVM: 70%). The SVM method outperformed the FLDA method. The results indicated that the integration of remote sensing information and meteorological information can promote the monitoring accuracy of plant diseases. |
| Keywords: | remote sensing meteorology powdery mildew habitat conditions disease monitoring |
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