安徽农业科学2011，Vol.39Issue(33)：20329-20331,20714,4.
基于高光谱成像技术的生菜叶片水分检测研究
Study on Detection of Moisture Content in Lettuce Leaves based on Hyperspectral Imaging Technology
摘要
Abstract
[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 1420 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.关键词
高光谱成像/遗传算法/偏最小二乘回归/含水率检测Key words
Hyperspectral imaging/Genetic algorithm/Partial least squares regression/Detection of mois分类
农业科技引用本文复制引用
张晓东,毛罕平,周莹,左志宇,高洪燕..基于高光谱成像技术的生菜叶片水分检测研究[J].安徽农业科学,2011,39(33):20329-20331,20714,4.基金项目
国家自然科学基金项目(61075036) （61075036）
中国博士后科学基金(20100481097) （20100481097）
江苏高校优势学科建设工程资助项目(苏财教[2011]8号) （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) （BM2009703）
江苏省高校自然科学基础研究重大项目(10KJA210010) （10KJA210010）
江苏大学高级专业人才基金项目(10JDG081) （10JDG081）
江苏大学博士后基金项目(201009). （201009）
