Climate Change Adaptation Through Smart Agriculture: A Multidisciplinary Framework Using IoT and Machine Learning

Jure Leskovec

Climate Change Adaptation Through Smart Agriculture: A Multidisciplinary Framework Using IoT and Machine Learning

Author(s)	Jure Leskovec
Country	United States
Abstract	Climate change has emerged as one of the most significant global challenges affecting agricultural productivity, food security, water availability, biodiversity, and rural livelihoods. Rising temperatures, erratic rainfall, prolonged droughts, floods, soil degradation, pest outbreaks, and extreme weather events have intensified the need for climate-resilient agricultural systems. Smart Agriculture, supported by the Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), remote sensing, Geographic Information Systems (GIS), cloud computing, unmanned aerial vehicles (UAVs), and precision farming technologies, has become a promising solution for enhancing agricultural sustainability and climate adaptation. IoT-enabled sensors continuously monitor soil moisture, temperature, humidity, nutrient levels, crop health, and environmental conditions, while machine learning algorithms analyse these data to support intelligent irrigation, crop prediction, disease detection, yield forecasting, and resource optimisation. This study investigates climate change adaptation through smart agriculture using a multidisciplinary framework integrating IoT and machine learning. A qualitative and analytical research methodology based on secondary data collected from peer-reviewed journals, international reports, government publications, and agricultural case studies was employed. The study examines precision agriculture, climate-smart farming, intelligent irrigation, remote sensing, crop monitoring, predictive analytics, decision support systems, and sustainable resource management. Furthermore, it evaluates the influence of smart agriculture technologies on agricultural productivity, water conservation, climate resilience, food security, environmental sustainability, and rural development while identifying implementation challenges and future research opportunities. The findings indicate that integrating IoT and machine learning significantly improves crop productivity, resource-use efficiency, early disease detection, irrigation management, climate risk prediction, and sustainable agricultural practices. The incorporation of Digital Twins, blockchain, edge computing, Explainable Artificial Intelligence (XAI), and satellite-based monitoring further enhances decision-making, supply chain transparency, and agricultural resilience. However, challenges including infrastructure limitations, digital literacy, cybersecurity, interoperability, high implementation costs, data privacy, and unequal technology access continue to affect widespread adoption. The study concludes that smart agriculture supported by IoT and machine learning provides an effective multidisciplinary framework for climate change adaptation and will play a critical role in achieving sustainable agriculture, resilient food systems, and the United Nations Sustainable Development Goals (SDGs).
Keywords	: Climate Change Adaptation, Smart Agriculture, Internet of Things, Machine Learning, Precision Agriculture, Sustainable Farming, Climate-Smart Agriculture, Remote Sensing, Food Security.
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2019
Published On	2019-01-08

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Climate Change Adaptation Through Smart Agriculture: A Multidisciplinary Framework Using IoT and Machine Learning

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