Machine learning and fluorosensing for estimation of maize nitrogen status at early growth-stages

•Fluorescence indices indicate discrimination capabilities of variable N rates in crop.•Assess accuracies of crop canopy N indicators estimated using machine learning model.•Model transferability in a cross-site experiment was assessed. Potential of mobile fluorescence sensor measurements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season. Real time estimation of such N status indicators at field scale would enable precision management of N fertilizers. In standard practice, linear regression analysis involves the use of several fluorescence channels and indices as predictive variables for estimating plant nitrogen content. Considering the multi-collinearity between these predictor variables, the conventional regression analysis (multiple linear regression) often fails to deliver a good range of prediction accuracies. Hence, machine learning regression techniques are utilized in this study to estimate N status indicators i.e., %N, above ground biomass, and N uptake at V6 and V9 growth stages of maize across three site-years in 2012 and 2013 crop growing seasons. The Multiplex®3 (FORCE-A) portable active fluorescence system was used to capture fluorescence information. Derived indices including four N balance indices (NBI_R, NBI_B, NBI_B, and NBI1), two chlorophyll indices (CHL and CHL1), and one flavonoid index (FLAV) were used as predictors. The independent site data were first utilized in a Support Vector Regression (SVR) model to assess the training and test accuracies in estimation of N status indicators considering a comparative analysis between V6 and V9 growth stages. The current research also involved assessing how well the machine learning-trained model could be applied to a different dataset and validated its performance in a cross-site experimental setting. Subsequently, cross-site comparisons of nitrogen status estimates were conducted to recommend the selection of machine learning strategies. These strategies include (1) Partial Least Square Regression, (2) Support Vector Regression, (3) Gaussian Process Regression, (4) Random Forest Regression, and (5) Artificial Neural Network. The comparative investigation demonstrated promising accuracy in estimating plant nitrogen content, above-ground biomass, and nitrogen uptake at the V6 stages of maize, with correlation coefficients in the moderate range (r = 0.72 ± 0.03) and Root Mean Square Error. Superior prediction accuracies were obtained at V9 grow