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基于Logistic、IBk以及Randomcommittee方法的条锈病潜育期小麦冠层光谱的定性识别

引用本文：刘琦,李薇,王翠翠,谷医林,王睿,马占鸿.基于Logistic、IBk以及Randomcommittee方法的条锈病潜育期小麦冠层光谱的定性识别.植物保护学报,2018,45(1):146-152

DOI：10.13802/j.cnki.zwbhxb.2018.2018915

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作者	单位	E-mail
刘琦	中国农业大学植物保护学院植物病理学系, 农业部作物有害生物监测与绿色防控重点实验室, 北京 100193 新疆农业大学农学院植物病理学系, 农林有害生物监测与安全防控重点实验室, 乌鲁木齐 830052
李薇	中国农业大学植物保护学院植物病理学系, 农业部作物有害生物监测与绿色防控重点实验室, 北京 100193
王翠翠	中国农业大学植物保护学院植物病理学系, 农业部作物有害生物监测与绿色防控重点实验室, 北京 100193
谷医林	中国农业大学植物保护学院植物病理学系, 农业部作物有害生物监测与绿色防控重点实验室, 北京 100193
王睿	中国农业大学开封实验站, 河南开封 475004
马占鸿	中国农业大学植物保护学院植物病理学系, 农业部作物有害生物监测与绿色防控重点实验室, 北京 100193	mazh@cau.edu.cn

中文摘要:为寻求在小麦条锈病潜育期能探知和监测病害的简单便捷方法，通过人工接种不同品种小麦诱发条锈病，在小麦条锈病菌尚处于潜育期时，采集小麦冠层光谱数据，并利用双重Real-timePCR分子生物学技术检测条锈病菌潜育菌量，基于Logistic、IBK以及Randomcommittee三种方法，在不同建模比、不同参数变换下建立可识别潜育期小麦条锈病的数学模型。结果表明，在全波段范围内（325~1 075 nm），3种方法所建模型模拟识别潜育期小麦条锈病是可行的，但识别效果有一定差异，基于Logistic、IBK以及Randomcommittee方法所建模型的平均准确率分别为83.95%~84.51%、87.72%~88.98%、93.19%~93.46%。因此，基于Randomcommittee方法所建模型的识别准确率最高，效果最好，更适合小麦条锈病潜育期的定性识别。

中文关键词:小麦条锈病潜育期高光谱监测分子检测

Qualitative identification of canopy spectra in wheat stripe rust based on Logistic, IBk and Randomcommittee methods

Author Name	Affiliation	E-mail
Liu Qi	Department of Plant Pathology, MOA Key Lab of Pest Monitoring and Green Management, College of Plant Protection, China Agricultural University, Beijing 100193, China Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests, Department of Plant Pathology, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, Xinjiang Uygur Autonomous Region, China
Li Wei	Department of Plant Pathology, MOA Key Lab of Pest Monitoring and Green Management, College of Plant Protection, China Agricultural University, Beijing 100193, China
Wang Cuicui	Department of Plant Pathology, MOA Key Lab of Pest Monitoring and Green Management, College of Plant Protection, China Agricultural University, Beijing 100193, China
Gu Yilin	Department of Plant Pathology, MOA Key Lab of Pest Monitoring and Green Management, College of Plant Protection, China Agricultural University, Beijing 100193, China
Wang Rui	Kaifeng Experimental Station of China Agricultural University, Kaifeng 475004, Henan Province, China
Ma Zhanhong	Department of Plant Pathology, MOA Key Lab of Pest Monitoring and Green Management, College of Plant Protection, China Agricultural University, Beijing 100193, China	mazh@cau.edu.cn

Abstract:To explore the rapid diagnosis method of wheat stripe rust during the latent period, different varieties of wheat were artificially inoculated by the Puccinia striiformis f. sp. tritici(Pst). The canopy hyperspectral data was collected in the latent period, and the amount of Pst was also obtained by using the duplex Real-time PCR. Based on the three methods of Logistic, IBk and Randomcommittee, the hyperspectral remote sensing mathematical models were established to recognize the wheat stripe rust during the latent period with different modeling ratio and different modeling parameters. The results showed that within the 325-1 075 nm waveband, the mathematical models based on the methods of Logistic, IBK and Randomcommittee to discriminate wheat stripe rust in the latent period was feasible. But a certain difference in the recognition effectiveness was also found. The average recognition accuracy of the Logistic, IBK and Randomcommittee methods were 83.95%-84.51%, 87.72%- 88.98%, 93.19%-93.46%, respectively. The results indicated that the mathematical model based on Randomcommitteemethod weremore suitable for qualitative identification of wheat stripe rust during the latent period.

keywords:wheat stripe rust latent period hyperspectral remote sensing molecular detection

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