An Empirical Study of Regression Algorithms for Soil Organic Matter Prediction
Date Issued
September 5, 2024
Author(s)
Abstract
Soil organic matter (SOM) is an important component that exists in soils because it is closely related to soil health and fertility. Hence, knowing the existence of SOM in soils is crucial for management corrections. So far laboratory analysis is required for SOM determination. However, such procedures are costly and labor-time consuming. Alternative methodologies for SOM determination are needed to achieve sustainability. The rise of artificial intelligence and machine learning provide promising approaches that can be exploited for this purpose. The aim of this study is to identify the best regression algorithm for SOM prediction for citrus planted soils. Several machine learning approaches are investigated, including adaptive boosting, gradient boosting, random forest, and multi-layer perceptron neural network.
Funding
The authors acknowledge the "EXCELSIOR": ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The "EXCELSIOR" project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology. The authors acknowledge the "AI-OBSERVER" project funded by the European Union’s Horizon Europe Framework Programme HORIZON-WIDERA-2021-ACCESS-03 (Twinning) under the Grant Agreement No 101079468.
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