A Study on the Productivity Enhancement Mechanism of AI-Based Agricultural Data Analysis Technology

This study aims to elucidate the specific mechanism through which AI-based agricultural data analysis technology enhances agricultural productivity. While previous studies have primarily focused on the necessity of AI adoption or presented isolated success cases, this research critically examines the fundamental limitations of the conventional prediction-optimization (open-loop) model and proposes a novel alternative, the Closed-Loop Autonomous Learning System. The proposed mechanism incorporates a cyclical process in which AI autonomously learns causal relationships by integrating both environmental and crop outcome data, generates optimized cultivation recipes, executes them independently, and continuously improves through feedback. Through case analyses, this study demonstrates that, unlike existing systems that remain at the level of information provision and decision support, AI can maximize productivity and efficiency when it functions as a central agent for knowledge generation and autonomous execution. The findings highlight that the key to advancing AI in agriculture lies not merely in improving prediction accuracy but in ensuring autonomy and iterative learning capacity. The academic significance of this study lies in presenting a new paradigm in which AI emerges as the primary agent of agricultural production processes, moving beyond simple technological applications. Practically, the study provides meaningful implications for the development of smart agriculture policies and strategies for effective adoption of AI technologies at the farm level.