[Baek Cheol-hyeon’s Focus] The flashy shell of ‘Agricultural AX,’ and farmers’ cynicism that won’t open their wallets

The government has been pouring out optimism day after day, declaring the ‘distribution of agricultural artificial intelligence (AI)’ and ‘digital transformation’ as a cure-all that can revive disappearing rural communities. It is a world where autonomous tractors plow fields without the slightest error, and the AI assistant ‘Isak-i’ analyzes the weather to create the optimal farming schedule. Judging only by government announcements, rural South Korea is already a hub of cutting-edge agtech rivaling Silicon Valley.

But behind the flashy stage, the bare reality faced by actual farmers is cold and unsparing. The results of a survey conducted by this newspaper for its anniversary, targeting the Korean Society for Agricultural Machinery and 36 experts—the country’s top authorities in agricultural machinery and smart farming—were shocking. The actual level of on-site adoption of agricultural AI and smart farming in Korea, as perceived by experts, was only 3.53 out of 10, while satisfaction with after-sales service (AS) scored a meager 3.58.

The reason the field is so cynical is clear. No matter how flashy the technology is, it simply does not produce the ROI (return on investment) needed to make farmers open their wallets. One farmer interviewed on site complained, “With fuel costs rising and income falling, when would I ever recover the cost of buying an expensive tractor? No matter how hard it is, I have no choice but to make do with my own labor.”

For advanced tractors and unmanned harvesting robots costing hundreds of millions of won, the average time needed to break even (BEP) without subsidies is 6.85 years. Given Korea’s unique conditions of small-scale, irregular plots fragmented to around 1 hectare, while farm income per pyeong remains stagnant, no farmer is willing to bear the risk of investing hundreds of millions of won for nearly seven years. Policymakers should take to heart the painful observation of one academic figure: “Farm households want ‘practical technology they can use right now,’ rather than futuristic cutting-edge technology for the distant future.”

An even bigger problem is the asymmetry of government policy that ‘distributed machines but missed people.’ In the field, when ‘digital breakdowns’ such as software firmware conflicts or cloud linkage failures occur, work often stops for days because no one can do anything about it. That is because the current rural AS infrastructure still remains at the level of traditional mechanical repair—tightening bolts with oil-stained wrenches.

Moreover, far from systematically training personnel by region, even city and county agricultural technology centers—the institutions closest to farmers—lack the ability to analyze and prescribe solutions based on the flood of digital data. Highly skilled young operators capable of running the technology turn away, blocked by a preference for cities and poor rural settlement conditions. No matter how many expensive imported robots are distributed with subsidies, if there is no one to repair them when they break and no one to operate them, they are not advanced equipment but merely costly ‘smart scrap metal.’

Now is the time to wake up from the illusion of ‘digitalizing every farm household’ and face reality with a cool head.

We must stop the current distribution method that effectively forces individual farmers to buy expensive machines, and urgently shift the policy paradigm toward ‘shared (rental-type) public goods’ centered on local government agricultural machinery rental centers or regional field management organizations. It is far more realistic to focus on supplying ‘simple, low-cost appropriate technology’ that even elderly farmers can use as easily as KakaoTalk with just a few words, and to activate ‘farm work proxy services’ for small farmers.

The value of technology is proven not on the clean monitors of the laboratory where it is developed, but in the rough rural fields where dust flies and rice straw scatters. The blunt criticism from the field—“Those drafting agricultural AI policy do not accurately understand AI”—must not be taken lightly. ‘Agricultural AX’ that is nothing but a shell, obsessed with filling distribution quotas, cannot help farmers. What is needed now is not flashy algorithms, but a thorough overhaul centered on ‘field acceptability’ that reduces farmers’ operating costs by even a single won and serves as the hands and feet of elderly farmers.