AI-ENABLED SMART AGRICULTURE FRAMEWORK USING MULTISENSOR FUSION ANALYTICS

Abstract

Smart agriculture increasingly relies on real-time environmental intelligence, crop health analysis, and predictive decision-making to optimize productivity and reduce resource wastage. This paper proposes an AI-enabled smart agriculture framework that integrates multisensor fusion analytics across soil, climate, crop, and machinery sensors to deliver accurate, timely insights. The system combines edge-based data acquisition, cloud-driven machine learning, and hybrid fusion strategies to improve detection accuracy, anomaly monitoring, and yield forecasting. Experimental evaluation using field-mimicking datasets shows enhanced performance in moisture prediction, pest detection, and fertilizer optimization. The framework significantly improves operational efficiency, reduces human intervention, and supports scalable and sustainable agricultural practices