Multivariate stacked regression pipeline to estimate correlated macro and micronutrients in potato plants using visible and near-infrared reflectance spectra

The ability to sense nutrient status in potato plants using spectroscopy has several merits including the ability to proactively respond to deficiencies of certain elements. While research so far has focused on finding spectral signatures of elements based on their foliar reflectance, the influence of the spectral signatures of the elements on each other in estimating their concentrations in the plant has not been investigated. This work presents a pipeline of stacked regression models capable of accurately estimating nutrient concentrations based on the foliar reflectance. A data set was built from 179 samples of petioles collected across two growing seasons, consisting of the chemical concentrations of 11 nutrients with spectral reflectance values between 400 and 2500 nm. The pipeline consisted of a base layer composed of a multiple univariate linear Lasso regression models to find the initial independent signatures of each nutrient, followed by a layer of nonlinear models to correlate these signatures and account for their interdependencies before finalizing the estimation. The results show that adding this second layer improved estimation performance for 10 and 9 nutrients out of 12 in the dried and fresh mode, respectively, with large improvements in predictive performance for some critical micronutrients such as Zn, Fe, and Al. • Intricate biological relationships of nutrients overlapped reflectance spectra. • Multivariate stacked regression was developed to estimate correlated nutrients. • Ensemble machine learning Combining Lasso and XGB provided optimum results. • The developed pipeline improved the estimation of all nutrients of fresh and dried leaves.