Machine Learning Approaches for Climate- Resilient Smart Agriculture Using Remote Sensing and IoT Technologies

Abstract

Agricultural production systems are increasingly vulnerable to climate variability, extreme weather events, and changing environmental conditions. These challenges necessitate the development of intelligent agricultural technologies capable of supporting adaptive decision-making and resource-efficient farming practices. Smart agriculture integrates Internet of Things (IoT) technologies, remote sensing data, and machine learning algorithms to monitor environmental conditions and optimize agricultural operations. This study proposes a machine learning–driven framework for climate-resilient smart agriculture that combines satellite-based remote sensing, ground-level IoT sensor networks, and predictive analytics. The framework continuously collects environmental parameters including soil moisture, temperature, humidity, rainfall, and vegetation indices derived from satellite imagery. Machine learning models are then used to predict crop health conditions, irrigation requirements, and potential climate-related risks. The research introduces a data processing architecture consisting of a sensing layer, data integration module, machine learning prediction engine, and agricultural decision support system. Several machine learning algorithms including Random Forest, Support Vector Machine, and Artificial Neural Networks are evaluated for their performance in crop yield prediction and climate stress detection. Experimental analysis using simulated agricultural datasets demonstrates that machine learning-based predictive models can significantly improve the accuracy of crop monitoring and irrigation scheduling. Results indicate that the proposed framework enhances crop yield prediction accuracy while reducing water consumption through intelligent irrigation recommendations. The findings highlight the potential of integrating remote sensing technologies and machine learning algorithms to support climate-resilient agricultural systems capable of adapting to environmental uncertainty and supporting sustainable food production.