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.