AI-Driven Agricultural Transformation: Bridging Informatization and Rural Revitalization

Abstract

The national strategy of Rural Revitalization, profoundly propelled by informatization, is catalyzing a technological transformation in the agricultural sector. This paper investigates the application and impact of Artificial Intelligence (AI) technology within this strategic context, aiming to bridge the gap between technological potential and practical implementation. Through a mixed-methods approach, we systematically analyze how AI-driven solutions—including computer vision for crop disease detection, IoT-sensor data analytics for precision irrigation, and predictive models for yield forecasting—are addressing core challenges in traditional agriculture. These challenges encompass low production efficiency, resource waste, and insufficient market connectivity. The study further develops a conceptual framework that situates AI as a core enabler within the informatization ecosystem, driving smart agriculture towards the strategic goals of industrial prosperity and ecological sustainability. While case studies demonstrate significant improvements in operational efficiency and decision-making support, the research also identifies critical barriers to widespread adoption. These include the high cost of technology deployment, the digital literacy gap among farmers, and issues of data sovereignty and interoperability. The paper concludes that the synergistic integration of AI with broader informatization policies is not merely a technical upgrade but a fundamental driver for sustainable rural development. It provides evidence-based insights for policymakers and stakeholders to navigate this digital transition effectively.