Библиографическое описание:Application of non-parametric learning method in soil suitability assessment in present day economy : научное издание / V. V. Kukartsev [et al.]. - Текст : непосредственный // Journal of Infrastructure, Policy and Development. - 2024. - Т. 8, № 7. - ISSN 2572-7923. - ISSN 2572-7931, DOI 10.24294/jipd.v8i7.4074.
Аннотация:<jats:p>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.</jats:p>
Источник:Journal of Infrastructure, Policy and Development
Выпуск:Т. 8, № 7
Держатель оригинала документа:Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia, Department of Information Economic Systems, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia, Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia
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