서울대학교 미주동창회 Seoul National University Alumni Association in the USA
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AI-based digital farming, often termed “precision agriculture,” revolutionizes traditional farming by integrating technologies like Artificial Intelligence (AI), Internet of Things (IoT) sensors, and data analytics. This transition moves agriculture from a generalized, reactive approach to a hyper-specific, proactive, and data-driven one.
The process begins with continuous data collection from a vast network of sensors deployed on drones, satellites, autonomous machinery, and embedded directly in the soil. These instruments gather real-time information on crop health, nutrient levels, soil moisture, pest presence, and hyper-local weather patterns.
This raw data is fed into sophisticated AI and machine learning algorithms, which analyze current conditions against vast historical datasets. This analysis generates actionable insights and predictive modeling. AI can predict disease outbreaks before they are visible, identify specific nutrient deficiencies, and calculate the optimal time for planting and harvesting.
These insights inform precision actions. For example, instead of blanket-spraying an entire field, variable-rate technology can apply water, fertilizer, or pesticides only where precisely needed, sometimes down to the level of an individual plant. The ultimate goal is to optimize productivity, significantly reduce resource waste (such as water and chemicals), improve sustainability, and increase overall profitability.
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