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 least squares 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。

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 hyperspectral data 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 those characteristic 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 reflect the 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 in pepper leaf, which provided a theoretical foundation for monitoring plant growth and distribution of nutrients.