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Using machine learning to enhance agricultural productivity in Turkey: insights on the importance of soil moisture, temperature and precipitation patterns

dc.contributor.authorAltan, M. Uzunoz
dc.contributor.authorNabatov, E.
dc.date.accessioned2026-06-27T15:07:03Z
dc.date.issued2024
dc.description.abstractThis article delves into the intricate details of a robust machine learning analysis that was conducted on a plethora of environmental data, including precipitation, temperature, soil moisture, and vegetation index data, in three distinct regions of Turkey, namely the Aegean, Southeastern Anatolia, and the Mediterranean. The core objective of this scientific inquiry is to shed light on the quintessential determinants that wield a profound influence on agricultural productivity, with an explicit focus on the soil's moisture, temperature fluctuations, and precipitation patterns. It is of paramount importance to fathom the intricacies and multifarious dimensions of these pivotal determinants to enrich our understanding of the entangled dynamics between the ecosystem and crop cultivation. The intricate nature of soil's moisture is a multifaceted interplay, encompassing water availability and the delicate interconnectedness between precipitation, soil structure, and vegetative growth, which can instigate a series of biological and chemical reactions. Furthermore, the study underscores the significance of monitoring the normalized difference vegetation index (NDVI) as a critical indicator of vegetation growth and yield. The outcomes of this study are truly fascinating and highlight the enormous potential of AI-powered tools, which incorporate advanced machine learning and deep learning models, in elevating and optimizing crop management practices, thereby leading to heightened crop productivity and profitability while promoting sustainability. The revolutionary discoveries made in this study underscore the tremendous potential of artificial intelligence (AI) technologies to propel and elevate the agricultural forecasting and management processes, resulting in a more sustainable, productive, and efficient agricultural industry that bestows substantial environmental, social, and economic advantages.en
dc.description.urihttps://doi.org/10.1007/s13762-023-05439-x
dc.identifier.doi10.1007/s13762-023-05439-x
dc.identifier.eissn1735-2630
dc.identifier.endpage6998
dc.identifier.issn1735-1472
dc.identifier.issue10
dc.identifier.startpage6981
dc.identifier.urihttps://hdl.handle.net/20.500.14981/68134
dc.identifier.volume21
dc.identifier.wos001156081000001
dc.language.isoeng
dc.publisherSPRINGER
dc.relation.ispartofINTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
dc.subjectMachine learning
dc.subjectAegean
dc.subjectSoutheastern Anatolia
dc.subjectMediterranean
dc.subjectCPC leaky bucket model
dc.subjectPrecision agriculture
dc.subjectEnvironmental Sciences & Ecology
dc.titleUsing machine learning to enhance agricultural productivity in Turkey: insights on the importance of soil moisture, temperature and precipitation patterns
dc.typeArticle
dspace.entity.typePublication
local.import.sourceWOS

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