B.Nandhini, Assistant Professor | DT- Case Study- AI in Precision Agriculture | SNS Institutions

Problem Background:
Precision agriculture aims to optimize crop yields while minimizing resources. Accurate crop yield prediction is crucial for informed decision-making. Traditional methods rely on historical data, weather forecasts, and manual observations, leading to inaccuracies and suboptimal resource allocation.
Problem Statement:
Develop an AI-driven system to enhance crop yield prediction in precision agriculture, addressing the limitations of traditional methods and improving resource allocation, profitability, and sustainability.
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