This study developed a quadratic discriminant analysis (QDA) model from the spectroradiometer reflections (400 to 1000 nm) and phosphorus (P) uptake in wheat under varying rates of P dosages (0, 25, and 50 ppm P) in the tillering (GS25) and heading (GS55) stages. Seventy-two experimental plants were grown under controlled greenhouse conditions. Stepwise multiple regression analysis was used to determine the wavelengths associated with different periods and P doses. Principal component analysis was employed to select the five wavelengths (418, 563, 639, 756, and 1000 nm) that best encompassed the total variance amongst the different reflection values. The QDA model assigned the training data to their real classes (0 ppm P: 79%, 25 ppm P: 50%, and 50 ppm: 83%) with 71% accuracy. For validation of the model, 36 randomly selected test data were used (0 ppm P: 75%, 25 ppm P: 42%, and 50 ppm P; 92%) and resulted in 69% accuracy. Results concluded that wheat P demand during different vegetation stages can be determined from the spectral wavelengths input into a QDA model; for future research, however, we suggest the nutrient dosage ranges are broad enough to provide sufficient variability. Nevertheless, discriminant modeling is a viable method of determining plant nutritional status by spectral data.

中文翻译：

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可见和近红外光谱判别分析法测定面包小麦叶中磷的含量

这项研究根据分radi（GS25）中不同P剂量（0、25和50 ppm P）比率的小麦的分光辐射计反射（400至1000 nm）和磷（P）吸收量建立了二次判别分析（QDA）模型。 ）和标题（GS55）阶段。在受控的温室条件下种植了72个实验植物。使用逐步多元回归分析来确定与不同时间段和P剂量相关的波长。主成分分析用于选择五个波长（418、563、639、756和1000 nm），这些波长最好地涵盖了不同反射值之间的总方差。QDA模型将训练数据分配给其真实类别（0 ppm P：79％，25 ppm P：50％和50 ppm：83％），准确性为71％。为了验证模型，使用了36个随机选择的测试数据（0 ppm P：75％，25 ppm P：42％和50 ppm P； 92％），结果准确性为69％。结果得出结论，可以根据输入到QDA模型中的光谱波长来确定不同植被阶段的小麦磷需求。对于未来的研究，我们建议营养剂的剂量范围足够宽，以提供足够的可变性。然而，判别建模是通过光谱数据确定植物营养状况的可行方法。