AI-ENABLED SMART AGRICULTURE FRAMEWORK USING MULTISENSOR FUSION ANALYTICS

Abstract

Smart agriculture increasingly relies on realtime 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, clouddriven machine learning, and hybrid fusion strategies to improve detection accuracy, anomaly monitoring, and yield forecasting. Experimental evaluation using fieldmimicking 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.