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作 者:谢传奇[1,2] 方孝荣[3] 邵咏妮[1] 何勇[1]
机构地区:[1]浙江大学生物系统工程与食品科学学院,杭州310058 [2]佛罗里达大学农业与生物工程系,盖恩斯维尔32611 [3]金华职业技术学院成人教育学院,金华321017
出 处:《农业机械学报》2015年第3期315-319,共5页Transactions of the Chinese Society for Agricultural Machinery
基 金: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;中央高校基本科研业务费专项资金资助项目(2014FZA6005)
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874-1 734 nm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、1609、1 656 nm)和9个(901、905、908、915、918、1 123、1 305、1 460、1 680 nm)特征波长,并建立EW-LS-SVM和EWLDA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。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 - 1 734 nm. Spectral reflectance of 30 x 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 (911 nm, 1 409 nm, 1 511 nm, 1 609 nm, 1 656 nm) and nine wavelengths (901 nm, 905 nm, 908 nm, 915 nm, 918 nm, 1 123 nm, 1 305 nm, 1 460 nm, 1 680 nm) 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.
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