To End the Tragedy of Plowing Under Onion Fields... The Core of Agricultural AI Is 'Consumption Data'

As prices of major agricultural products such as onions, cucumbers, and Korean zucchini plunged this year, farmers held a rally on July 7, plowing under the fields they had carefully cultivated and urging the government to come up with countermeasures. While artificial intelligence (AI) and autonomous tractors are emerging as the future of rural communities, the reality in the fields remains harsh. This year, the tragedy was repeated once again. Experts point out that without sophisticated on-site “data” to support it, it is impossible to prevent imbalances in agricultural supply and demand and price collapses. This is why, in the era of AI transformation, the need to build not only production data but also “consumption data” is emerging as the most urgent task for Korean agriculture to survive.

The Ministry of Agriculture, Food and Rural Affairs and the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry held the 40th Future Growth Forum for the Agriculture and Food Industry on July 22 at JW Marriott Hotel Seoul under the theme, “Prospects for Agricultural Technology in the Age of AI Transformation: Discussing the AX Transformation of the Agriculture and Food Industry.” Kim Dae-hoon, CEO of Nuvilab, who spoke that day, sharply pointed out the contradictory reality of the agri-food industry. CEO Kim said, “The global agri-food market is the world’s largest at about 9.7 trillion dollars (BCG estimate, 2026 outlook), but its digital transformation (DX) maturity remains at the bottom compared with other industries such as finance, manufacturing, healthcare, and media.”

Because of this “digital and data vacuum,” producers cannot know final consumer demand or market trends even if they produce high-quality agricultural products. Government officials and policymakers likewise can only grasp subsidy support or agricultural supply policies, but they lack data to verify what results those policies actually produced in the field. Because the data chain linking production, distribution, and final consumption is broken, farmers, the government, and consumers all suffer headaches every year from repeated instability in supply and demand.
CEO Kim Dae-hoon emphasized, “Without consumption data, optimizing agri-food production is virtually impossible,” adding, “Sales data are only traces of demand. Optimization of the food industry begins only when actual consumption data exist.”

The key to AI is ‘field-context data’… implications from overseas success stories
In the end, AI that collects data precisely at agricultural sites and at points of consumer contact is bound to be more competitive. Overseas, sophisticated AI industrialization is already being achieved based on field data.
In fact, at 13 hotels in the United Arab Emirates (UAE), AI cameras and weight sensors are analyzing discarded food in real time to improve production volume, serving volume, and menu composition in real time. After a four-month pilot operation, they achieved a 76% reduction in kitchen waste and a 55% reduction in plate waste after meals.

Australia’s fresh food distribution platform Freshō converted an ordering system that had relied on handwritten entries into an AI-based automated order structuring system. By precisely forecasting real-time demand, it raised inventory accuracy to 98.6% and minimized excess inventory and waste through accurate ordering, packing, and delivery. As a result, it processed a cumulative 30 million orders (about 10 million in the past year) and attracted 17 million dollars in investment.
 

The ripple effect of 2.3 trillion won from 1% consumption optimization
In particular, CEO Kim Dae-hoon highlighted the value of “consumption data,” which had long remained in the blind spot of agricultural policy. In his presentation, he explained, “Serving 30% vegetables in group meals does not mean children ate 30% vegetables,” adding, “According to data actually collected through AI technology, children’s actual vegetable intake rate was only 11%, creating a gap of 19 percentage points. There are clear structural reasons why such consumption data had not accumulated until now.”

He pointed out that standardized data collection was difficult because the amount and form of food consumed vary every time, and methods such as 24-hour recall or self-entry inevitably have low accuracy and continuity. He also said retention rates were low because the service structure was input-centered, requiring users to spend a great deal of time leaving records, and that data in each field—school meals, nutrition, and healthcare—were fragmented, making integrated analysis impossible.

CEO Kim Dae-hoon stressed, “Even optimizing consumption by just 1% by filling the consumption data gap can create enormous economic and environmental value,” adding, “Based on the domestic food market (about 230 trillion won, Korea Development Institute 2024), achieving 1% consumption optimization can generate cost savings of up to 2.3 trillion won.” He continued, “This figure includes reduced food ingredient waste, optimized inventory and logistics, and lower medical expenses due to improved health,” and added, “It can also deliver tremendous environmental results by reducing greenhouse gas emissions by up to 1.39 million tons (CO_2e, Ministry of Environment 2023 standard).”

CEO Kim Dae-hoon’s statement, “Abundant data creates AI performance and changes the future of industry. The AI transformation of the food industry begins the moment actual consumption is turned into data,” carries significant implications. Now is the time to build a “national integrated agri-food data platform” that connects everything from growth data at the production stage to distribution volumes and final consumption data on the dining table. Only when data continuity and field relevance are guaranteed can the government’s agricultural policy finally shift from a simple “supply policy” to a substantive “performance-oriented policy.”