利用数码相机和成像光谱仪估测棉花叶片叶绿素和氮素含量

实时、无损监测棉花叶片的叶绿素和氮素含量对诊断棉花生理状况和氮肥精确管理具有重要意义。本研究基于MSI200成像光谱仪和数码相机两种可见光传感器，分析和比较了光谱和颜色参数与叶绿素、氮素浓度和SPAD读数的关系，并且确立了其定量预测模型。结果表明，不同传感器对叶绿素和氮素最敏感的波段分别为R₇₁₀和R；光谱指数与叶绿素、氮素浓度和SPAD读数的相关性比原始光谱好，而且以蓝光和红光波段组成的差值指数(DI和R–B)的预测能力最佳；DI所建棉花叶片Chl a+b、Chl a、Chl b、N和SPAD读数的预测模型的预测误差分别为0.0058、0.0050、0.0018和2.3002 mg g^–1和4.9736(分别为均值的18.39%、19.47%、30.33%、11.69%和8.45%)，预测精度R²分别为0.7965、0.7582、0.6608、0.7019和0.7338；R–B所建模型的预测性比DI差，对Chl a+b的预测精度最高(R²=0.7400)，而预测Chl b的精度最低(R²=0.5653)。基于CIE 1976 L*a*b*颜色模型的颜色参数b*和HSI颜色模型的S是两种传感器与叶绿素、氮素浓度和叶色关系较好的颜色参数；b*对叶绿素、氮素浓度和SPAD读数的预测能力稍逊于DI，预测误差和精度都与DI的比较接近；而饱和度S值的预测RRMSE最大，整体预测精度小于0.62。因此，可以利用可见光成像传感器的光谱和颜色参数估测棉花叶片叶绿素和氮素含量。

Estimation of Chlorophyll and Nitrogen Contents in Cotton Leaves Using Digital Camera and Imaging Spectrometer

Leaf chlorophyll and nitrogen concentrations of cotton (Gossypium hirsutum L.) are important indicators of plant N status. They can provide valuable insights into the physiological performance of leaves. The objectives of this study were to determine the relationships between chlorophyll, nitrogen, SPAD readings and leaf spectral and color parameters in cotton. Spectral and color parameters for the non-destructive estimation of chlorophyll, nitrogen contents and SPAD readings were obtained by using digital camera (Olympus C-5060) and imaging spectrometer (MSI200) so a wide range of them was established in cotton. The dataset was separated into two parts using for calibration (n=100) and validation (n=60), respectively. Therefore, a systematic analysis was undertaken on quantitative relationships of chlorophyll, nitrogen, SPAD readings to major spectral indices, such as the ratio index (RI), normalized difference index (ND) and difference index (DI), composed of any two wavelengths with original reflectance and color parameters. The results indicated that the maximum sensitivity of reflectance to variation in chlorophyll, nitrogen contents and SPAD readings was found in the far-red wavelength region at 710 nm and in the red wavelength region (R) for two sensors, respectively. Furthermore, spectral indices could improve the prediction ability obviously, and difference indices (DI and R-B) of different sensors composed of blue and red wavelengths gave a better prediction performance. The models to retrieve chlorophyll, nitrogen contents and SPAD readings using DI were the most feasible models with the maximum determination coefficients (R²) and the minimum RMSE, especially, DI (R₄₄₀, R₇₁₀), DI (R₄₄₀, R₇₁₀), DI (R₄₂₀, R₇₁₀), DI (R₄₂₀, R₇₂₀) and DI (R₄₉₀, R₇₁₀) were the optimum indices for the models of chlorophyll a+b, chlorophyll a, chlorophyll b and N, and SPAD readings, respectively. R-B was the optimum index of digital camera but its prediction performances were lower than these of DI. Additional, b* (CIE 1976 L*a*b* color model) and S (HSI color model) were the optimum color parameters, and the prediction ability of b* was lower than that of DI. However, the prediction performance of S was relative weak with the highest RRMSE and the lowest R². Thus, measurements of leaf reflectance and color in visible range by using digital camera and imaging spectrometer may provide a real-time and accurate means of estimating leaf chlorophyll and nitrogen contents and monitoring of cotton plant nitrogen status and N fertilizer management in the field.