Machine Learning Approaches for Sustainable Agriculture and Precision Farming
Abstract
Sustainable agriculture is crucial for ensuring food security while minimizing environmental impact. Machine learning has emerged as a transformative tool in precision farming, enabling data-driven decision-making for crop monitoring, yield prediction, and resource management. This paper examines various ML techniques, including deep learning, computer vision, and reinforcement learning, in optimizing irrigation, soil health analysis, and pest control. By utilizing satellite imagery and IoT sensors, ML models can predict climate variations and optimize farming practices, reducing resource wastage. Our research presents a comparative analysis of ML-based agricultural solutions and their effectiveness in promoting sustainability in food production.
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