Тип публикации: статья из журнала
Год издания: 2024
Идентификатор DOI: 10.24294/jipd.v8i7.4074
Ключевые слова: sustainable growth, land cover change, land degradation, land use, soil quality
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 agriculturaПоказать полностьюl 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>
Журнал: Journal of Infrastructure, Policy and Development
Выпуск журнала: Т. 8, № 7
ISSN журнала: 25727923
Издатель: EnPress Publisher, LLC