Spatiotemporal variability of nitrogen nutrition index in potato fields: A UAV-based machine learning approach using a Bayesian critical nitrogen dilution curve

Abstract

Nitrogen (N) management is one of the main factors enhancing potato productivity and promoting sustainable agricultural practices. The Nitrogen Nutrition Index (NNI, obtained as the ratio of actual plant N, to the critical plant N concentration) is widely applied to assess the N status of various crops. Traditionally, NNI is calculated using field data, but remote sensing (RS) technologies can offer more rapidly and timely assessment of the spatiotemporal (within field) variability of this index. This study employs multispectral data acquired via Unmanned Aerial Vehicle (UAV) and machine learning (ML) models to estimate potato NNI. A Bayesian hierarchical partially pooled method was fitted to a three-year field experiment in Denmark and extensive ground-based potato datasets to model the critical nitrogen dilution curve (CNDC) and calculate the NNI. Multispectral UAV data were processed to extract four spectral bands and calculate several vegetation indices, which were used as predictors to train and test six ML models: Linear regression, support vector machines, gaussian process regression, stepwise linear regression, ensemble trees and neural networks. Among the compared models, gaussian process regression outperformed, showing R2 equal to 0.83 and a RMSE of 0.10 and providing accurate NNI predictions, comparable to ground-based Bayesian estimates. The variability of the NNI was analyzed over the seasons using 28 NNI maps derived from UAV surveys at spatial resolution of 0.04–0.09 m/pixel, capturing spatial variations in crop N status over time. The proposed framework, designed for NNI prediction at the intra-field scale, has the potential to be adapted to different environments and crops. The framework can support practical decisions for precision N management, reducing the environmental impact of potato cultivations and enhancing sustainability.