Leveraging Machine Learning and Data Mining: Enhancing Agricultural Productivity and Sustainability (SDG 2 Zero Hunger)

Abstract

This research explores the potential of machine learning (ML) and data mining (DM) in enhancing agricultural productivity and sustainability, aligned with the goals of Sustainable Development Goal 2 (Zero Hunger). The study examines how these advanced technologies optimize agricultural practices through predictive analytics, early detection of pests and diseases, and resource management, particularly in water and soil use. The integration of ML and DM promotes precision agriculture, allowing for data-driven decisions that improve yield while minimizing environmental impacts. It highlights the applications and challenges of implementing these technologies, especially in developing regions where data accessibility, technical infrastructure, and expertise are limited. Furthermore, the study underscores the importance of sustainable farming practices and offers insights into overcoming the barriers to widespread ML and DM adoption. By leveraging these technologies, agriculture can move toward increased productivity, reduced resource consumption, and enhanced food security.