Optimizing fertilizer usage in agriculture with AI Driven Recommendations

DOI: https://doie.org/10.0805/Jbse.2024577123

Prof Jayarani, Prof.Channa Basava, Prof.Nagaraj C, Prof.Kusuma Latha D

Keywords:

Artificial Intelligence, Machine Learning, Fertilizer Optimization, Precision Agriculture, Sustainable Farming, Crop Yield Prediction, Environmental Sustainability, Soil Health, Predictive Analytics, Smart Farming.

Abstract:

The efficient use of fertilizers is crucial for sustainable agriculture, ensuring high crop yields while minimizing environmental impacts. This study explores the application of artificial intelligence (AI) in optimizing fertilizer use through AI-driven recommendations. By integrating machine learning algorithms with comprehensive soil and crop data, we developed a predictive model that offers precise fertilizer application rates tailored to specific field conditions. The model leverages historical agricultural data, real-time environmental sensors, and advanced analytics to provide actionable insights for farmers. Results indicate a significant reduction in fertilizer usage and cost, with maintained or improved crop yields. This approach not only enhances productivity but also promotes environmental sustainability by reducing the risk of nutrient runoff and soil degradation. The findings underscore the potential of AI in transforming fertilizer management practices, paving the way for more intelligent and sustainable agriculture.

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