Application of non-parametric learning method in soil suitability assessment in present day economy

Vladislav Kukartsev, Andrei Gantimurov, Kirill Kravtsov, Aleksey Borodulin, Yadviga Tynchenko

Article ID: 4074
Vol 8, Issue 7, 2024

VIEWS - 147 (Abstract) 76 (PDF)

Abstract


This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.


Keywords


sustainable growth; land cover change; land degradation; land use; soil quality

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References


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DOI: https://doi.org/10.24294/jipd.v8i7.4074

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