AI-Based Smart Farming: Integrating Robotics, Sensors, and Big Data with a Legal Policy for Sustainable Agriculture

The agricultural industry is developing in a datatrained, accurate, long-lasting system with rapid integration of robotics, AI, sensors, and large information. In AI-based smart farming, computer vision, machine learning, and prophecy analysis are assigned to enable high crop production and quality, while robotics automates serious processes such as planting, irrigation, harvesting, and weed management, reduces labor dependence, and enhances efficiency. Sensors and the Internet of Things provide real-time data on environmental conditions, crop health, and soil. Big data and cloud platforms process this data to generate useful insights for improved decisions. These innovations significantly improved the efficiency of the resources by using 40% low water and 30% low fertilizer to reduce the loss of greenhouse gas emissions and after harvest. However, a strong legal and strategic framework is required to relieve high implementation costs, data privacy and ownership, farmers' skill distance and infrastructure limit in rural areas to deploy such technology. Moreover, when formulating policies, the farmers of smallholder farmers should consider the moral aspects of justified access and employment displacement. The free legal administration integrated with the AI-proceeded smart system ensures consensus, fairness, and technical efficiency. In line with the Sustainable Development Goals of the UN, this study makes the case that agricultural systems can be made climate resilient, ecologically conscious, and socially just by fusing technical innovation with supportive policy. Therefore, with the support of strong legal frameworks, AI-based smart farming has the potential to revolutionize global agriculture and guarantee long-term food security.