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《Journal of Anhui Agricultural Sciences》 2011-33
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Study on Detection of Moisture Content in Lettuce Leaves based on Hyperspectral Imaging Technology

ZHANG Xiao-dong et al(Key Laboratory of Modern Agricultural Equipment and Technology,Ministry of Education and Jiangsu Province,Jiangsu University,Zhenjiang,Jiangsu 212013)  
[Objective] The study aimed to explore the method for detecting the moisture content of the crop by hyperspectral imaging technology.[Method] With Italy lettuce with the bolting resistance in whole year as the tested materials,the hyperspectral images of lettuce leaves were collected by the hyperspectral imaging system and treated by ENVI V.4 and Matlab V 7.0 software.[Result] The optimal characteristic wavelength was 1 420 nm optimized from the selected hyperspectral images of lettuce leaves by using the adaptive band selection.The images of all samples at 1 420 nm were segmented,reversed and operated and then the target images were obtained.From each target image,the mean value and standard deviation of the gray scale were extracted as the gray feature,and the mean value and standard deviation of energy,entropy,moment of inertia and correlation were extracted as the texture feature.The optimal feature subset was selected by GA-PLS,and the partial least-squares regression model was established on base of the optimal characteristic to detecting the moisture content of the lettuce leaves.[Conclusion] The correlation coefficient between the predict value and the real value was 0.902,whose precision was obviously higher than the prediction models based on gray or texture feature.
【Fund】: 国家自然科学基金项目(61075036);; 中国博士后科学基金(20100481097);; 江苏高校优势学科建设工程资助项目(苏财教﹝2011﹞8号);; 江苏省农业装备与智能化高技术研究重点实验室项目(BM2009703);; 江苏省高校自然科学基础研究重大项目(10KJA210010);; 江苏大学高级专业人才基金项目(10JDG081) 江苏大学博士后基金项目(201009)
【CateGory Index】: S636.2
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