MACHINE LEARNING-ENABLED AGRICULTURE: A REVIEW OF INTELLIGENT SOLUTIONS FOR FOOD SECURITY AND SUSTAINABLE FARMING

The rapid growth of the global population, climate change, and the increasing demand for agricultural
productivity have intensified the need for innovative and sustainable farming practices. Machine learning (ML),
a key branch of artificial intelligence, has emerged as a transformative technology capable of addressing
complex agricultural challenges through data-driven decision-making. This review explores the diverse
applications of machine learning in modern agriculture, focusing on its role in enhancing food security and
promoting sustainable farming systems. Key areas examined include crop yield prediction, disease and pest
detection, soil health assessment, irrigation management, weed identification, precision farming, and
agricultural robotics. The review highlights commonly employed machine learning algorithms, including
supervised, unsupervised, and deep learning models, and evaluates their effectiveness in processing large-scale
agricultural datasets derived from sensors, drones, satellite imagery, and Internet of Things (IoT) devices.
Furthermore, the study discusses the benefits of ML-driven agricultural solutions, such as improved resource
utilization, reduced environmental impact, increased productivity, and enhanced decision support for farmers.
Challenges related to data quality, model interpretability, infrastructure limitations, and technology adoption are
also critically analyzed. Finally, future research directions are outlined, emphasizing the integration of advanced
machine learning techniques with emerging digital agriculture technologies. The findings demonstrate that
machine learning has significant potential to revolutionize agriculture and contribute to global food security and
environmental sustainability.