Vegetation Biomass and Nitrogen Content Estimation Using Ensembles: Regression and Active Learning Strategies for Field Sampling Reduction

Optimization of agricultural practices is crucial to ensure high and sustainable food production. In this regard, hyperspectral remote sensing demonstrated its usefulness in the building of models able to predict several vegetation parameters. The effort, in terms of time and costs, of a remote sensing campaign depends on both the instruments to be exploited and the number of samples to be acquired for model calibration. Therefore, the reduction of field activities is important to limit monitoring costs. The purpose of this work is to demonstrate that the combination of appropriate active learning strategies and regression models can dramatically reduce the field work with negligible degradation of model prediction performance. This has been assessed by processing two different datasets. In the first experiment, estimated ryegrass biomass and nitrogen content resulted in average root-mean-square error (RMSE) values of 0.17 t/ha and 3.22 kg/ha against the best literature values, on the same dataset, of 0.20 t/ha and 4.68 kg/ha. In the second test, the estimation of maize biomass and nitrogen content with a reduced calibration dataset provided, respectively, an RMSE of 0.65 t/ha and 11.1 kg/ha against a reference value of 7.1 kg/ha concerning the estimation of nitrogen content. Depending on the settings, the developed methodology provides results comparable with the literature or negligibly degraded, despite a reduction of the calibration dataset up to 80%.