Instance-based transfer learning for soil organic carbon estimation

Abstract

Soil organic carbon (SOC) is a vital component for sustainable agricultural production. This research investigates the transfer learning-based neural network model to improve classical machine learning estimation of SOC values from other geochemical and physical soil parameters. The results on datasets based on LUCAS data from 2015 showed that the Instance-based transfer learning model captured the valuable information contained in different source domains (cropland and grassland) of soil samples when estimating the SOC values in arable cropland areas. The effects of using transfer learning are more pronounced in the case of different source (grassland) and target (cropland) domains. Obtained results indicate that the transfer learning (TL) approach provides better or at least equal output results compared to the classical machine learning procedure. The proposed TL methodology could be used to generate a pedotransfer function (PTF) for target domains with described samples and unknown related PTF outputs if the described samples with known related PTF outputs from a different geographic or similar land class source domain are available