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高光谱成像技术的不同叶位尖椒叶片氮素分布可视化研究
引用本文:余克强,赵艳茹,李晓丽,丁希斌,庄载椿,何勇. 高光谱成像技术的不同叶位尖椒叶片氮素分布可视化研究[J]. 光谱学与光谱分析, 2015, 35(3): 746-750. DOI: 10.3964/j.issn.1000-0593(2015)03-0746-05
作者姓名:余克强  赵艳茹  李晓丽  丁希斌  庄载椿  何勇
作者单位:浙江大学生物系统工程与食品科学学院,浙江 杭州 310058
基金项目:国家高技术研究发展计划(863计划)项目
摘    要:为了快速、准确、直观估测尖椒叶片的营养水平和生长状况,利用高光谱成像技术结合化学计量学方法对不同叶位尖椒叶片氮素含量(nitrogen content, NC)的分布进行了可视化研究。按照叶片位置采摘尖椒叶片,并采集高光谱数据,然后测定相应叶片的SPAD和NC。提取出叶片的光谱信息后,采用Random-frog(RF)算法提取特征波段,分别选出5条与10条特征波段。针对选取的特征波段和全波段,分别建立偏最小二乘回归(partial leastsquares regression, PLSR)模型,结果表明采用特征波段建立的PLSR模型性能较好(SPAD:RC=0.970, RCV=0.965, RP=0.934; NC: RC=0.857, RCV=0.806, RP=0.839)。根据预测模型计算尖椒叶片高光谱图像每个像素点的SPAD与NC,从而实现SPAD与NC的可视化分布。事实上叶片的SPAD在一定程度上可以反映含氮量,二者分布图的变化趋势基本一致,验证了可视化结果的正确性。结果表明:运用高光谱成像技术可以实现对不同叶位尖椒叶片氮素分布的可视化研究,这为监测植物的生长状况和养分分布提供理论依据。

关 键 词:高光谱成像技术  Random-frog算法  叶片叶位  氮素含量  可视化   
收稿时间:2014-01-18

Application of Hyperspectral Imaging for Visualization of Nitrogen Content in Pepper Leaf with Different Positions
YU Ke-qiang,ZHAO Yan-ru,LI Xiao-li,DING Xi-bin,ZHUANG Zai-chun,HE Yong. Application of Hyperspectral Imaging for Visualization of Nitrogen Content in Pepper Leaf with Different Positions[J]. Spectroscopy and Spectral Analysis, 2015, 35(3): 746-750. DOI: 10.3964/j.issn.1000-0593(2015)03-0746-05
Authors:YU Ke-qiang  ZHAO Yan-ru  LI Xiao-li  DING Xi-bin  ZHUANG Zai-chun  HE Yong
Affiliation:College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Abstract:In order to estimate pepper plant growth rapidly and accurately, hyperspectral imaging technology combined with chemometrics methods were employed to realize visualization of nitrogen content (NC) distribution. First, pepper leaves were picked up with the leaf number based on different leaf positions, and hyperspectraldata of these leaves were acquired. Then, SPAD and NC value of leaves were measured, respectively. After acquirement of pepper leaves’ spectral information, random-frog (RF) algorithm was chosen to extract characteristic wavelengths. Finally, five characteristic wavelengths were selected respectively, and then thosecharacteristic wavelengths and full spectra were used to establish partial least squares regression (PLSR) models, respectively. As a result, SPAD predicted model had an excellent performance of RC=0.970, RCV=0.965, RP=0.934, meanwhile evaluation parameters of NC predicted model were RC=0.857, RCV=0.806, RP=0.839. Lastly, according to the optimal models, SPAD and NC of each pixel in hyperspectral images of pepper leaves were calculated and their distribution was mapped. In fact, SPAD in plant can reflectthe NC. In this research, the change trend of both was similar, so the conclusions of this research were proved to be corrected. The results revealed that it was feasible to apply hyperspectral imaging technology for mapping SPAD and NC inpepper leaf, which provided a theoretical foundation for monitoring plant growth and distribution of nutrients.
Keywords:Hyperspectral imaging  Random-frog algorithm  Leaf position  Nitrogen content  Visualization
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