AI-Enabled Smart and Precision Agriculture: Smart Sensors, IoT, Deep Learning, Computer Vision, and Decision Support: A Comprehensive Survey

Artificial intelligence (AI) is increasingly integrated with smart sensors, Internet of Things (IoT) networks, machine learning, deep learning, and computer vision to support site-specific agricultural management. However, strong performance on computational benchmarks does not necessarily translate into reliable decisions, effective field interventions, or measurable improvements in agricultural productivity and resource efficiency. This comprehensive narrative survey critically synthesizes advances in agricultural sensing, IoT architectures, data processing, multimodal sensor fusion, predictive modeling, computer vision, decision optimization, and automated actuation. Particular emphasis is placed on the relationships between measurement reliability, model generalization, decision feasibility, intervention accuracy, and verified agricultural outcomes. The reviewed literature demonstrates promising capabilities in crop monitoring, yield prediction, disease and weed recognition, precision irrigation, and selected automated operations. Nevertheless, heterogeneous evaluation protocols, limited independent field validation, sensor calibration challenges, distribution shifts, interoperability barriers, and deployment costs constrain operational reliability and generalizability. To address these interconnected challenges, the survey proposes a literature-informed reference architecture linking agricultural observations, data integration, AI inference, constrained decision-making, physical interventions, and outcome feedback, supported by uncertainty assessment, human oversight, and cross-layer assurance. The proposed architecture is conceptual and has not been empirically validated. Future research priorities include reproducible field evaluations, representative agricultural datasets, uncertainty-aware and explainable AI, resource-efficient computing, interoperable infrastructures, and economically inclusive deployment. The review concludes that the practical value of AI-enabled precision agriculture should be evaluated through reliable interventions, sustainable resource utilization, and demonstrated agricultural outcomes rather than predictive performance alone.