谢传奇,方孝荣,邵咏妮,何 勇.番茄叶片早疫病近红外高光谱成像检测技术[J].农业机械学报,2015,46(3):315-319.
Xie Chuanqi,Fang Xiaorong,Shao Yongni,He Yong.Detection of Early Blight on Tomato Leaves Using Near-infrared Hyperspectral Imaging Technique[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(3):315-319.
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番茄叶片早疫病近红外高光谱成像检测技术   [下载全文]
Detection of Early Blight on Tomato Leaves Using Near-infrared Hyperspectral Imaging Technique   [Download Pdf][in English]
投稿时间:2014-04-04  
DOI:10.6041/j.issn.1000-1298.2015.03.046
中文关键词:  番茄 早疫病 近红外光谱 格拉姆斯密特模型 贝叶斯罗蒂斯克回归 最小二乘-支持向量机
基金项目:国家高技术研究发展计划(863计划)资助项目(2013AA102301)、高等学校博士学科点专项科研基金资助项目(20130101110104)、教育部留学回国人员科研启动基金资助项目和中央高校基本科研业务费专项资金资助项目(2014FZA6005)
作者单位
谢传奇 浙江大学
佛罗里达大学 
方孝荣 金华职业技术学院 
邵咏妮 浙江大学 
何 勇 浙江大学 
中文摘要:提出了基于格拉姆斯密特(MGS)模型和贝叶斯罗蒂斯克回归(BlogReg)的近红外高光谱成像技术检测番茄叶片早疫病的方法。利用高光谱图像采集系统获取波长874~1734nm范围内70个染病和80个健康番茄叶片的高光谱图像,选取染病和健康叶片30像素×30像素感兴趣区域的光谱反射率。建立了番茄叶片早疫病的最小二乘-支持向量机(LS-SVM)识别模型,再通过MGS和BlogReg提取特征波长(EW),分别得到5个(911、1409、1511、1609、1656nm)和9个(901、905、908、915、918、1123、1305、1460、1680nm)特征波长,并建立EW-LS-SVM和EW-LDA模型。在所有模型中,建模集的正确识别率为93%~98%,预测集的正确识别率为96%~100%。结果表明,近红外高光谱成像技术检测番茄叶片早疫病是可行的,MGS和BlogReg都是有效的特征波长提取方法。
Xie Chuanqi  Fang Xiaorong  Shao Yongni  He Yong
Zhejiang University;University of Florida,Jinhua Polytechnic,Zhejiang University and Zhejiang University
Key Words:Tomato Early blight Near-infrared spectroscopy Modified gram-schmidt model Bayesian logistic regression Least square-support vector machines
Abstract:Early detection of early blight on tomato leaves using NIR hyperspectral imaging technique based on modified gram schmidt (MGS) model and Bayesian logistic regression (BlogReg) were studied. Hyperspectral images of 70 infected and 80 healthy tomato leaves were acquired by hyperspectral imaging system in the spectral wavelength of 874~1734nm. Spectral reflectance of 30×30 pixels from region of interest (ROI) of hyperspectral image was extracted. Least squares-support vector machine (LS-SVM) model based on the full wavelength was established to detect early blight. Five (911nm, 1409nm, 1511nm, 1609nm, 1656nm) and nine wavelengths (901nm, 905nm, 908nm, 915nm, 918nm, 1123nm, 1305nm, 1460nm, 1680nm) were selected by MGS and BlogReg, respectively. Then, LS-SVM and linear discriminant analysis (LDA) models were built based on these effective wavelengths. Among these models, the correct classification rates were 93%~98% in calibration set and 96%~100% in prediction set, respectively. The result indicated that it was feasible to detect early blight on tomato leaves by using NIR hyperspectral imaging technique.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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