The use of precision farming has become a transformative approach to optimizing crop production while ensuring environmental sustainability. Fertilizer application, a critical factor in agricultural productivity, is often performed using conventional methods that lead to inefficient resource utilization, soil degradation, and environmental pollution. This paper presents an AI-based fertilizer recommendation system that leverages machine learning algorithms, remote sensing data, and soil health parameters to provide precise nutrient recommendations tailored to specific crops and field conditions. The proposed model integrates data from soil sensors, weather forecasts, and historical yield patterns to generate accurate fertilizer prescriptions, reducing excess usage and enhancing crop yields. By employing AI- driven predictive analytics, the system optimizes nutrient distribution, minimizes environmental impact, and supports sustainable farming practices. Experimental results demonstrate the effectiveness of the model in improving fertilizer, efficiency while maintaining soil health and maximizing agricultural productivity. This research highlights the potential of AI in precision agriculture and provides insights into how intelligent decision-making systems can contribute to sustainable food production will focus on expanding the datasets, refining predictive models, and integrating real-time monitoring for enhanced accuracy and adaptability.
Peddinti et al. (Tue,) studied this question.
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