Exploring the Potential of Machine Learning in Agriculture: A Review of its Applications and Results

Abstract

This review paper provides an overview of the applications of machine learning in the agriculture field. Machine learning, a subfield of artificial intelligence, has been successfully applied to various domains, and agriculture is no exception. The paper starts with a brief introduction to machine learning and its various algorithms. It then presents various applications of machine learning in agriculture, including crop yield prediction, precision agriculture, plant disease detection, and soil moisture prediction. The paper highlights the advantages of using machine learning in agriculture, including increased efficiency, reduced costs, and improved decision-making. It also discusses the challenges faced in the application of machine learning in agriculture, including the need for large amounts of data and the difficulty in collecting high-quality data in remote and rural areas. Finally, the paper concludes with future directions for research and the potential impact of machine learning on the agriculture industry. The review shows that machine learning has the potential to revolutionize the way we approach agriculture and food production, leading to a more sustainable and efficient future for the industry.