ABSTRACT

Grain protein content (GPC), as an important agronomic indicator of nutritional value, is commonly used for optimized cultivation, classified harvesting, and quality grading. Previous studies on the spectroscopic estimation of GPC were often conducted at the canopy level with vegetation indices (VIs) derived from the visible and near-infrared (VNIR) bands, which are apart from the absorption features of protein predominately located in the shortwave infrared (SWIR) region. Since those absorption features are mostly masked by water for living plants, the causal relationships between sensitive spectral bands and protein absorption features are poorly understood. Recently, continuous wavelet spectral analysis has been commonly applied to the spectroscopic estimation of vegetation parameters in the way of extracting the optimal wavelet feature (denoted as WF_λ,S, λ is the shifting factor in wavelength and S is the scaling factor in the power number of two) or the red-edge position. This study proposed the wavelet-based SWIR edge position (SWEP) for estimating the GPC in cereal crops through extending the red edge position extraction technique to laboratory SWIR reflectance spectroscopy. The performance of SWEPs and WFs was evaluated in coefficient of determination (R²), root mean squared error (RMSE) and relative RMSE (RRMSE) with experimental data over field trials.

Our results demonstrated that the protein absorption features in the SWIR region could be enhanced using wavelet-based methods towards accurate estimation of the GPC across rice and wheat crops. The optimal features WF_1510,4 (R² = 0.96) and WF_1610,5 (R² = 0.93) exhibited similar performance for GPC estimation as compared to normalized difference protein indices (NDPIs) on calibration data, but the latter NDPI models had much poorer performance on independent validation data (NDPI_1745,1780: R² = 0.35, RMSE = 1.83%, RRMSE = 21.70%; NDPI_2080,2160: R² = 0.57, RMSE = 1.48%, RRMSE = 17.55%). The GPC could be estimated with high accuracies on both calibration (R² = 0.94) and validation (R² = 0.92, RMSE = 0.64%, RRMSE = 7.59%) using the SWEP extracted from the 1450–1650 nm range at Scale 5. Given their close predictive performance, the wavelet-based SWEP was less affected by the degradation of spectral resolution in the input data than the WF. The application of SWEPs could help us better understand the spectroscopy mechanism of GPC estimation from grain reflectance spectra. It also suggests that not just the commonly used red edges but also the insufficiently exploited SWIR edges are important in the entire spectral domain for quantifying vegetation properties.

摘要

谷物蛋白含量（GPC）作为营养价值的重要农艺指标，通常用于优化栽培，分级收获和质量分级。以前关于GPC光谱估计的研究通常在冠层进行，植被指数（VIs）来自可见和近红外（VNIR）波段，除了主要位于短波红外中的蛋白质的吸收特征（ SWIR）区域。由于对于活植物，这些吸收特征大部分被水掩盖，因此人们对敏感光谱带与蛋白质吸收特征之间的因果关系了解得很少。最近，_λ，S，λ是波长的移动因子， S是2的幂次或红边位置的比例因子。这项研究提出了基于小波的SWIR边缘位置（SWEP），通过将红色边缘位置提取技术扩展到实验室SWIR反射光谱法来估计谷物作物中的GPC。SWEP和WF的性能通过测定系数（ R ²），均方根误差（RMSE）和相对均方根误差（RRMSE）以及野外试验的实验数据进行评估。

我们的结果表明，使用小波基方法可以提高水稻和小麦作物中GPC的准确估计，从而增强SWIR区域中的蛋白质吸收特征。最佳特征WF _1510,4（R ²  = 0.96）和WF _1610,5（R ²  = 0.93）与标准数据的标准化差异蛋白质指数（NDPI）相比，表现出相似的GPC估计性能，但后者的NDPI模型具有独立验证数据的性能要差得多（NDPI _1745,1780：R ²  = 0.35，RMSE = 1.83％，RRMSE = 21.70％; NDPI _2080,2160：R ² = 0.57，RMSE = 1.48％，RRMSE = 17.55％）。 使用从规模5的1450–1650 nm范围提取的SWEP，可以在校准（R ²  = 0.94）和验证（R ² = 0.92，RMSE = 0.64％，RRMSE = 7.59％）上以高准确度估算GPC 。鉴于其小波的预测性能，与WF相比，基于小波的SWEP受输入数据中光谱分辨率下降的影响较小。SWEPs的应用可以帮助我们更好地了解从谷物反射光谱估算GPC的光谱机理。这也表明不仅是常用的红色边缘，而且利用不足的SWIR边缘在整个光谱域中对于量化植被特性也很重要。