Abstract Predicting grain yield and protein content of maize using spectral reflectance data is very important for improved agricultural production. In this study, we predicted the grain yield and protein content of maize grown under different irrigation and nitrogen levels in 2018 and 2019 based on canopy spectral reflectance measurements at the V6 (sixth leaf), VT (tassel), and R2 (blister) stages. We developed a predictive approach, namely, spectral reflectance–physiological parameters–productivity, to predict grain yield and protein content on maize crop. First, the quantitative relationships between grain yield and protein content and physiological parameters (canopy chlorophyll content [CCC], leaf carbon accumulation [LCA], leaf nitrogen content [LNC], and leaf nitrogen accumulation [LNA]) were analysed. Then, vegetation indices (VIs) and wavelet features based on spectral reflectance were used to establish estimation models for physiological parameters. The physiological parameters were used as a bridge to connect the spectral reflectance data with grain yield and protein content. The purpose was to establish spectral inversion models to indirectly estimate grain yield and protein content. Results showed that grain yield had a significant linear relationship with CCC and LCA. In addition a grain protein content and LNC and LNA were also significantly related under different water, and nitrogen availability. The physiological parameter models with ratio vegetation indices (RVI), biorthogonal 3.3 (bior3.3), and reverse biorthogonal 1.5 (rbio1.5) were reliable in terms of predictability and applicability. Independent data verification suggested that grain yield was predicted using RVI769,758 (R2 = 0.773, RMSE = 2.509) in water availability, rbio1.5781,29 (R2 = 0.744, RMSE = 2.850) in nitrogen availability, and rbio1.5772,11 (R2 = 0.506, RMSE = 2.297) in water–nitrogen availability. In addition, the grain protein content was predicted using RVI793,757 (R2 = 0.704, RMSE = 0.744) in water availability, bior3.3743,19 (R2 = 0.717, RMSE = 0.957) in nitrogen availability, and RVI492,418 (R2 = 0.715, RMSE = 1.224) in water–nitrogen availability. Therefore, maize grain yield and protein content can be accurately predicted using our modelling approach.

中文翻译：

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利用冠层反射率预测不同水氮水平下玉米产量和蛋白质含量

摘要 利用光谱反射率数据预测玉米的籽粒产量和蛋白质含量对于提高农业生产非常重要。在本研究中，我们基于 V6（第六叶）、VT（雄穗）和 R2（水泡）阶段的冠层光谱反射率测量值预测了 2018 年和 2019 年在不同灌溉和氮水平下种植的玉米的籽粒产量和蛋白质含量. 我们开发了一种预测方法，即光谱反射率-生理参数-生产力，来预测玉米作物的谷物产量和蛋白质含量。首先，分析了籽粒产量与蛋白质含量和生理参数（冠层叶绿素含量[CCC]、叶碳积累[LCA]、叶氮含量[LNC]和叶氮积累[LNA]）之间的定量关系。然后，植被指数（VI）和基于光谱反射率的小波特征被用来建立生理参数的估计模型。生理参数被用作连接光谱反射率数据与谷物产量和蛋白质含量的桥梁。目的是建立光谱反演模型以间接估计谷物产量和蛋白质含量。结果表明，籽粒产量与CCC和LCA呈显着线性关系。此外，不同水分、氮素利用率下，籽粒蛋白质含量与LNC和LNA也呈显着相关。具有比植被指数（RVI）、双正交3.3（bior3.3）和反向双正交1.5（rbio1.5）的生理参数模型在可预测性和适用性方面是可靠的。独立数据验证表明，使用 RVI769,758 (R2 = 0.773, RMSE = 2.509) 的水分可用性、rbio1.5781,29 (R2 = 0.744, RMSE = 2.850) 和 rbio1.5772,11 预测谷物产量(R2 = 0.506, RMSE = 2.297) 水-氮可用性。此外，使用 RVI793,757 (R2 = 0.704, RMSE = 0.744) 的水分可用性、bior3.3743,19 (R2 = 0.717, RMSE = 0.957) 和 RVI492,418 (R2 = 0.715，RMSE = 1.224）水-氮可用性。因此，使用我们的建模方法可以准确预测玉米籽粒产量和蛋白质含量。297) 在水-氮可用性方面。此外，使用 RVI793,757 (R2 = 0.704, RMSE = 0.744) 的水分可用性、bior3.3743,19 (R2 = 0.717, RMSE = 0.957) 和 RVI492,418 (R2 = 0.715，RMSE = 1.224）水-氮可用性。因此，使用我们的建模方法可以准确预测玉米籽粒产量和蛋白质含量。297) 在水-氮可用性方面。此外，使用 RVI793,757 (R2 = 0.704, RMSE = 0.744) 的水分可用性、bior3.3743,19 (R2 = 0.717, RMSE = 0.957) 和 RVI492,418 (R2 = 0.715，RMSE = 1.224）水-氮可用性。因此，使用我们的建模方法可以准确预测玉米籽粒产量和蛋白质含量。