Recent Trends in AI and Data Science for Smart and Sustainable Agriculture: Enhancing Global Food Security

Artificial Intelligence (AI) and Data Science (DS) are revolutionizing agriculture by enabling precision farming, predictive analytics, and resource optimization to address food security amid climate challenges and population growth. This review paper explores recent trends (2023–2026) in smart and sustainable agriculture, focusing on AI applications in crop monitoring, yield prediction, pest detection, smart irrigation, and climate-resilient decision support. Key technologies—machine learning (e.g., CNNs, RNNs), computer vision, IoT integration, drones, and satellite imagery—are analyzed through a systematic literature review of 50+ recent papers, highlighting their impact on yield improvement (up to 20–30%), resource savings (e.g., 90% less herbicides), and sustainability metrics like reduced emissions and water use. Case studies demonstrate real-world deployments, such as AI-driven yield mapping and autonomous weeding. Challenges including data scarcity, high costs for smallholders, model interpretability, and ethical concerns are discussed, alongside future directions like multimodal AI, edge computing, and federated learning for equitable access. By synthesizing these advancements, this paper underscores AI's pivotal role in achieving UN Sustainable Development Goal 2 (Zero Hunger) through data-driven, resilient farming systems.