Data Science in Agriculture: Precision Farming and Beyond

Abstract

Agriculture is undergoing a transformation driven by data science, with precision farming techniques leading the way. This paper explores how data science, combined with agronomy, environmental science, and robotics, can optimize crop yields, reduce resource use, and enhance sustainability. Topics include the use of satellite imagery, soil sensors, and machine learning algorithms for predictive analytics in agriculture. Case studies from Australian farms highlight the potential of data science to address challenges such as climate change and food security. The paper concludes with recommendations for fostering collaboration between data scientists, farmers, and policymakers to scale these innovations.