Accurate crop yield prediction is essential for ensuring food security, improving agricultural productivity, and supporting farmers in decision-making. Traditional crop yield estimation methods are often inaccurate, time-consuming, and dependent on manual observations. With the advancement of Artificial Intelligence (AI) and machine learning (ML), data-driven approaches can significantly enhance prediction accuracy. This paper proposes an AI-based crop yield prediction model using machine learning algorithms. Various agricultural factors such as rainfall, temperature, humidity, soil properties, fertilizer usage, and historical crop yield data are analyzed. Machine learning models including Linear Regression, Decision Tree, Random Forest, and Support Vector Machine are implemented and compared. Experimental results demonstrate that the Random Forest model achieves the highest prediction accuracy. The proposed system provides an effective tool for farmers and policymakers to improve agricultural planning and optimize resource utilization.

This paper presents an Artificial Intelligence-based crop yield prediction model using machine learning algorithms. The proposed system demonstrates the effectiveness of AI techniques in agricultural applications. The results highlight the potential of machine learning models in enhancing crop yield prediction accuracy.

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