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Analysis of soilsuitability foragricultural needsusing machinelearning methods Kurashkin SergeiKravtsov KirillKukartsev Anatoly2024 год

Analysis of soil suitability for agricultural needs using machine learning methods
доклад, тезисы доклада, статья из сборника материалов конференций

Дополнительные ссылки: eLibrary · Источник статьи
База данных: Каталог библиотеки СФУ
Библиографическое описание: Analysis of soil suitability for agricultural needs using machine learning methods : доклад, тезисы доклада / S. S. Kurashkin [et al.]. - [S. l. : s. n.], 2024. - Текст : непосредственный // BIO WEB OF CONFERENCES : International Scientific and Practical Conference on Genetic Resources (Genetic Resources 2024) / International Scientific and Practical Conference on Genetic Resources (Genetic Resources 2024) (2024 ; 19.09 - 22.09 ; Divovo, Russia). - Les Ulis, 2024. - 149. - P. 01053. - DOI 10.1051/bioconf/202414901053.
Аннотация: <jats:p>This study explores the application of machine learning methods to assess soil suitability for agricultural purposes, focusing on identifying and analysing key factors that influence soil productivity under drought conditions. A classification model was developed based on data from diverse U.S. regions, which included critical soil parameters such as root condition, nutrient availability, soil toxicity, and oxygen accessibility for plant roots. Correlation analysis identified the most significant factors impacting soil suitability, with root condition and nutrient availability emerging as primary determinants. The model achieved a high accuracy of 98.81%, demonstrating its effectiveness in predicting soil suitability across varying environmental conditions. This research highlights the value of machine learning in optimizing agricultural practices by enabling data-driven soil assessment. Moreover, the study addresses the importance of accessible and cost-effective data collection methods for scaling the model to different regions. Despite the model's high accuracy, limitations related to data specificity and regional variability were observed, indicating areas for future improvement. Expanding the model with additional climatic and geographic data could enhance its generalizability and applicability in diverse agricultural settings. Overall, this study provides insights into the potential of machine learning to support sustainable agriculture and efficient land management.</jats:p>
Год издания: 2024
Авторы: Kurashkin Sergei, Kravtsov Kirill, Kukartsev Anatoly, Boyko Andrey, Volneikina Ekaterina
Издательство: Les Ulis
Источник: BIO WEB OF CONFERENCES
Выпуск: 149
Мероприятие: International Scientific and Practical Conference on Genetic Resources (Genetic Resources 2024) (Divovo, Russia, 2024 ; 19.09 - 22.09)
Номера страниц: 01053
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

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