To maximize agricultural operations and ensure increased productivity, sustainability, and efficiency, precision agriculture makes use of innovative technologies. This review article examines the incorporation of artificial intelligence, machine learning, automation, cloud computing, and Internet of Things sensors as they relate to agrarian engineering. IoT sensors are essential to the collection of real-time data because they offer insightful information about crop status, weather patterns, and soil health. The efficient data processing, storage, and accessibility made possible by cloud computing make scalable and real-time analytics possible. While automation technologies, like robotics and autonomous systems, improve operational efficiency and save labor costs, AI and ML help with predictive analytics, illness diagnosis, and fertilization process optimization. This study showcases case studies that demonstrate how these technologies have been successfully implemented, emphasizing the observable advantages in terms of increased yield, reduced costs, and resource conservation. It also goes into the technological, financial, and privacy issues that come with combining various technologies, it provides information on existing constraints and possible workarounds. In addition, the assessment points out new directions for research, including the deployment of 5G, developments in edge computing, and the creation of increasingly complex AI algorithms. Graphical Abstract

Precision agriculture is one of the ways to achieve food security and sustainability through better resource-use optimization and crop productivity dealing with the challenges posed by the growing population and addressing environmental concerns. The study offers an in-depth look at the most recent developments in artificial intelligence (AI) and automation in precision agriculture (PA), with a particular emphasis on important technologies such as drones, autonomous tractors, AI-driven irrigation systems, and predictive analytics for crop management. The accuracy of crop monitoring and health assessments has increased by 30–50 percent as a result of AI-powered solutions, which have improved resource-based decision-making. Systems for precision irrigation and fertilization have increased crop yields by 5–15 percent when using 25–40 percent less water and 30-40 percent less fertilizer, respectively. Robotic harvesters and sprayers are examples of automation technologies that have reduced labor expenses by 20–40 percent and increased operational efficiency by 35 percent. Additionally, AI-based prediction models have reduced pest damage by 20–25 percent and reached an accuracy of 85–90 percent for crop yield forecasts and pest control. Despite these developments, issues of scalability, affordability for small farms, and data privacy still exist, which can hinder technology adoption among farmers. The evaluation follows by outlining ideas for future research, such as 5G, blockchain, and AI integration with cloud and edge computing. These technologies could improve decision-making and transparency in precision agriculture by enabling real-time data transmission, secure data management, and enhanced traceability, thus addressing current limitations and fostering trust among stakeholders.