Nitrate Content in Open Field Spinach, Applicative Case for Hyperspectral Reflectance Data

Spinach, leafy vegetables with growing demand and high nutritional value, has a heightened focus on nitrate content. An open-field experiment evaluated the potential of vis-NIR-SWIR hyperspectral data for classifying spinach nitrate content. Shallow artificial neural networks (ANN) and ensemble techniques—majority voting (MV) and stacked generalization (stacked)—were applied. The competitive adaptive reweighted sampling (CARS), its stability version (SCARS), Elastic Net, and modified boosted versions of each (CARSplus, SCARSplus, and ENplus) were used as feature selection methods. ANNs were optimized for hidden layer size. The resulting models were further used in ensemble techniques by grouping them into two sets: one with all models and another with models trained using the three boosted feature selection subsets (fifty-three wavelengths). The best-performing ANNs were based on the SCARS, SCARSplus, and full datasets, achieving an accuracy (Acc) of 0.83. While the majority voting approach did not improve performance (Acc 0.82), the stacked ensemble models reached Acc 0.88. Notably, stacked performed well also with models trained on 53 wavelengths, demonstrating strong potential for transferability as the required sensors would be less complex than those used in this study. Furthermore, a simulation of the practical application was conducted using Italian Ministry of Health official data with the scope of showing a potential use case in improving nitrate management and for advancing efficient farming practices in agriculture. The stacked models demonstrated their utility in doubling the monitoring capacity for internal quality assurance in spinach farming within a regulated framework.