Artificial Intelligence and Machine Learning in Smart Farming: A Comprehensive Review

Abstract

The global agricultural sector faces unprecedented challenges due to population growth, resource scarcity, climate change, and the demand for sustainable practices. Smart farming, enabled by Artificial Intelligence (AI) and Machine Learning (ML), represents a promising paradigm shift from traditional methods to data-driven agriculture. This review comprehensively explores AI and ML applications in crop management, precision irrigation, yield forecasting, pest control, autonomous systems, and agri-supply chain optimization. It also discusses enabling technologies such as IoT, edge computing, and big data analytics. Additionally, we delve into the socio-economic implications, limitations, and emerging innovations shaping the future of agriculture. The article concludes by identifying knowledge gaps and proposing future directions for research and development.