Non-destructive potato petiole nitrate-nitrogen prediction using chlorophyll meter and multi-source data fusion with machine learning

In-season nitrogen (N) management is a promising strategy to achieve high tuber yield/quality and N use efficiency in potato (Solanum tuberosum L.) production. The SPAD-502 chlorophyll meter (SPAD) provides relative readings on plant N status using leaf chlorophyll transmittance and has the potential to replace the traditionally used expensive petiole analysis by estimating petiole nitrate-N (PNN) concentration non-destructively. The objective of this study was to develop a robust machine learning (ML) model for PNN concentration prediction across various genetic, environmental, and management conditions. Plot-scale experiments were conducted on an irrigated loamy sand soil in central Minnesota using a number of varieties and N fertilizer sources, application methods, and rates between 2010 and 2022. In each plot, approximately 20 petiole samples were collected for laboratory analysis, and 20 SPAD readings were collected and averaged. Weather information was collected by a nearby weather station. Three ML models (i.e. Random Forest, Extreme Gradient Boosting, and Support Vector Regression) were trained using Bayesian optimization in a nested 5-fold cross-validation. A near-linear trend was found between PNN concentration and the selected important features. Random Forest and Extreme Gradient Boosting regression models demonstrated that PNN concentrations could be predicted with an R2 of 0.8 using 15 features in a new site-year. When simplified by only using SPAD readings, cultivar information, accumulated growing degree days, accumulated total moisture, and as-applied N rates, these two tree-based models maintained the R2 values and achieved a 75 % diagnostic accuracy, outperforming both simple regression (66 %) and multivariate linear regression (70 %) models. We found that potato N status could be diagnosed accurately through PNN concentration prediction using chlorophyll meter and multi-source data fusion. The results of this study can be used as a baseline for future research on in-season N status diagnosis of potatoes involving different proximal and remote sensing technologies and N stress indicators.