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The potential of machine learning methods for the prediction of soil compaction

Pieczarka Krzysztof S., Pentoś Katarzyna, Cieniawska Beata, Mbah Jasper

Journal of Research and Applications in Agricultural Engineering

2025

Vol. 70, nr 1

5--11

The practice of precision farming is contingent upon a comprehensive understanding of the spatial variability of a multitude of physical and chemical soil parameters. The acquisition of knowledge regarding soil parameters necessitates the undertaking of soil sampling and subsequent analysis, a process that is inherently labour-intensive and time-consuming. Consequently, precision farming employs the identification of homogeneous field regions through the utilisation of scanning techniques, with the objective of ascertaining soil electrical characteristics, including electrical conductivity and magnetic susceptibility. The objective of this study was to attempt to predict soil compaction based on selected electrical parameters. In order to predict compaction, machine learning methods, namely decision tree and support vector regression were employed. The highest R-value of 0.87 was obtained for the decision tree model and soil layer 0.1-0.2 m for the training set. For the test set, the highest R-value of 0.85 was obtained for soil layer 0.1-0.2 m and the support vector regression model, which also had the lowest MAPE error value of 11.31%. The prediction of soil compaction using electrical soil parameters based on machine learning methods represents a promising avenue of research.

Optimisation of the fertilisation process for selected field spreaders

Cieniawska Beata, Pentoś Katarzyna, Pieczarka Krzysztof S., Kluza Paweł A., Dereń Katarzyna, Surma Mariusz, Jałoszyński Klaudiusz

Journal of Research and Applications in Agricultural Engineering

2024

Vol. 69, nr 2

48--56

Fertiliser application is one of the most important operations in agricultural production. It helps to increase the quality and quantity of the crop. However, the addition of too many ingredients or an unbalanced nutrient profile has a negative effect on crops. It is therefore important to apply fertiliser rationally and to achieve the correct level and uniformity of fertiliser distribution. The aim of this study was to develop a new model for lateral distribution uniformity during fertilisation. The tests were carried out under field conditions in a winter wheat crop. The quality of operation of three two-disc fertiliser spreaders at a travel speed of 1.22 m·s-1 was investigated. A Lagrange interpolation model was used to analyse the data. The accuracy of models was very high (R2 > 0.985). The models developed can be used in practice to facilitate control of the spreader operation, which will help to ensure uniform fertiliser distribution.