Published Paper Details:

Agriculture is the primary source of food for the global population. It provides essential nutrients and sustenance for billions of people worldwide. Without agriculture, there would be no reliable food supply, leading to hunger and malnutrition. Agriculture plays a significant role in the economy, source of Raw Materials, Biodiversity and Ecosystem Services etc. This paper explores the Real-World uses of Machine Learning (ML) in sustainable agriculture to confront the difficulties of a growing global population and climate change. With traditional methods suffering from reduced effectiveness due to changing climate patterns and rising food demands, ML offers a data-centric approach to revolutionize crop management. The study investigates ML algorithms such as neural networks, support vector machines, decision trees, and ensemble models to analyze key factors like soil quality, climate conditions, and past crop performance. The aim is to optimize crop selection for specific regions, resulting in higher yields and reduced environmental impact.