Instance-based transfer learning for soil organic carbon estimation
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2022
Чланак у часопису (Објављена верзија)
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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 sample...s 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