| Issue |
BIO Web Conf.
Volume 113, 2024
XVII International Scientific and Practical Conference “State and Development Prospects of Agribusiness” (INTERAGROMASH 2024)
|
|
|---|---|---|
| Article Number | 04008 | |
| Number of page(s) | 6 | |
| Section | Soil Monitoring, GIS, and Agroecology | |
| DOI | https://doi.org/10.1051/bioconf/202411304008 | |
| Published online | 18 June 2024 | |
Machine learning for chemical-humus correlation in soil
Moscow Aviation Institute (National Research University), 125993, Volokolamskoe shosse, 4, Moscow, Russia
* Corresponding author: lebedev.ivan.ig@yandex.ru
This article investigates the dependency of the quantitative content of humus in soil on phosphate (P2O5), potassium oxide (K2O), hydrolytic acid, as well as the pH value in aqueous and saline environments through machine learning. Linear regression was chosen as the primary model. The mean absolute error (MAE) was found to be 0.517, mean squared error (MSE) – 0.460, and the coefficient of determination after cross-validation reached 0.685. The search for the most significant covariate among the listed ones identified hydrolytic acid as the most impactful due to its influence on microbial activity in the soil and metabolism.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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