Leveraging Artificial Intelligence for Precision Agriculture: A Multisensor Data Fusion Approach
Abstract
Precision agriculture harnesses modern technologies to optimize crop production and resource management. This study proposes an AI-driven multisensor data fusion framework that integrates satellite imagery, drone-based sensors, and IoT soil moisture data to improve crop yield prediction and pest detection. Using machine learning algorithms, the framework analyzes heterogeneous data sources to provide actionable insights for farmers. Experimental results on wheat fields demonstrate a 15% increase in yield prediction accuracy and early identification of pest outbreaks, showcasing the potential of AI to revolutionize sustainable farming practices.
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