| Summary: | Nitrogen (N), phosphorous (P), and potassium (K) are important macronutrients to crops. Deficiencies of these nutrients can change the pigment content in leaves and affect photosynthesis, resulting in the similar spectral characteristics at some wavelengths. Thus, one of the most important challenges in crop nutrient stress assessment through the canopy’s spectral reflectance is the ability to discriminate different nutrient stress conditions. This study proposes a three-layer ensemble-modeling framework to analyze N, P, and K nutrient stresses utilizing canopy hyperspectral data of crops. The framework selects spectral bands that are sensitive to N, P, and K nutrient deficiency levels, using ensembles of random forest classifiers, and then the reflectance of the selected bands is transformed into the more distinguishable probability features to diagnose the N, P, and K nutrient deficiency levels. For this study, this proposed framework was applied to winter oilseed rape (<i>Brassica napus</i> L.) during the overwintering stage, with 915 spectra samples collected from 14 field experiments. The analysis of nutrient deficiency levels resulting from the proposed framework was compared with that of single random forest, support vector machine, and artificial neural network classifiers, using the same reflectance features selected in the first layer of the framework. The overall accuracy of the nutrient deficiency analysis achieved by the proposed framework reached 80.76%, which was 16.55%, 18.43%, and 35.74% higher than the single random forest, support vector machine, and artificial neural network classifiers, respectively. The proposed framework demonstrated competitive advantages in differentiating the medium deficiency of N and K, and the severe deficiency of K from the normal conditions, boosting the accuracy from below 25% to above 50% because the probability features enhanced the differences among nutrient deficiency levels.
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