DATA SCIENCE FOR PRECISION AGRICULTURE: LEVERAGING BIG DATA AND MACHINE LEARNING FOR CROP YIELD OPTIMIZATION

Abstract

Precision agriculture (PA) is revolutionizing modern farming practices by leveraging data science, big data analytics, and machine learning to optimize crop yield and resource usage. With the growing global demand for food, the agricultural sector faces significant challenges in maximizing productivity while minimizing environmental impact. This paper explores how big data and machine learning algorithms are employed to enhance crop yield, optimize irrigation, reduce waste, and improve pest control. It discusses the integration of sensors, satellites, and IoT devices to collect real-time data from farms and analyze this data using advanced data science techniques. The findings suggest that the adoption of data-driven approaches in precision agriculture can lead to sustainable farming practices, improved productivity, and cost reduction. Moreover, while challenges such as data quality, cost of implementation, and accessibility remain, the potential benefits for farmers and the environment are substantial.