[Anniversary Special Survey] Worrying Digital Divide in Rural Areas… ‘On-the-Ground Acceptability’ Is the Answer

The government is accelerating the spread of agricultural AI (artificial intelligence) and smart farming technologies to address labor shortages in rural areas. But in the field, concerns are being raised about a so-called “rural version of the digital divide” between large farms with capital and small farms without it.

Critics say that to prevent small farmers lacking the capital and capacity to adopt technology from being left out of the agtech ecosystem, a major policy shift centered on “on-the-ground acceptability” that farmers can easily feel and use is needed. This concern from the field was clearly reflected in the expert survey conducted by this newspaper.

“Don’t buy expensive equipment—share it instead”… paradigm shift to ‘shared public goods’
Experts said the most realistic way to include small farmers is to move away from the existing method of directly supplying individual farms and instead build a community-use system at the regional level. That is because a structure in which each farm personally owns high-priced advanced equipment costing hundreds of millions of won cannot embrace small and mid-sized farms with limited capital.

In fact, when asked which method is more suitable for Korean agriculture—individual distribution of agtech equipment to each farm or a shared distribution model through agricultural machinery rental centers and the like—55.6% (20 people) of respondents (36 people) chose the shared distribution model. The individual distribution model accounted for 19.4% (7 people), while 19.4% (7 people) said individual and shared models should be used in parallel depending on conditions such as farm size.

Experts emphasized, “AI should be shifted from something individual farms purchase to a jointly used service in the form of a public good,” adding, “Policy should pivot toward establishing regional AI agricultural support centers and moving from sharing equipment to sharing AI models themselves.” In other words, an open, shared ecosystem is needed in which public infrastructure centered on local governments or regional units provides hardware rentals and standardized AI services.

As concrete ways to use public infrastructure, experts proposed expanding joint-use systems for equipment and services centered on agricultural machinery rental centers, agricultural corporation associations, and local agricultural cooperatives. These include broadening the scope of machinery rental centers to encourage participation by small-scale farmers, developing and distributing practical AI agricultural machinery suited to small and medium-scale farm work such as transport vehicles, and grouping several small farmers together so they can share AI or robots. In addition, advice is gaining traction that the subsidy system, currently focused on equipment purchases, should be diversified to include subscription service fees, training costs, and maintenance expenses, thereby guaranteeing small farmers a real “opportunity to use technology.”
 

“Technology you have to study to use will fail”… focus on smartphone-based ‘simple, low-cost AI’
Given the reality of extreme aging in rural areas, there were also many calls to provide simplified services that can bridge the gap in technology accessibility. One expert pointed out, “The key to preventing the digital divide is not how flashy the technology is, but whether it is acceptable in the field,” adding, “For small farmers, AI should not be a difficult technology they have to study to use, but a convenient service that lets them enjoy the benefits with just one phone call or one messenger app.”

Most experts recommended first applying essential appropriate technologies (Low-cost AI) suited to small plots of farmland rather than forcibly transplanting large-scale big data systems centered on large farms. In other words, simple, low-cost models that intuitively provide only the core information farmers truly need—such as early pest and disease detection alerts or crop growth conditions—would be more efficient.

Since even elderly farmers use KakaoTalk and YouTube in their daily lives, creating a familiar platform environment that communicates by voice—like the Rural Development Administration’s conversational platform “Isak-i”—is being cited as an alternative. A system that summarizes and shows only the essential decision-making information through a platform that can be operated as easily as a weather app is seen as attractive.

Specific government-led pilot project models were also presented as alternatives. Representative examples include this year’s pilot project related to the “Representative Smart Farm Model for Small and Medium-Sized Farms” and next year’s planned “Nationwide Demonstration Project for a Standard Smart Farm Model for Small and Medium-Sized Farms.” This model is designed around “Ara Greenhouse,” a standards-based open integrated greenhouse management platform. Because it allows parts compatibility at low cost and lets users download and use needed AI services from an “agricultural app store,” it is drawing attention as a breakthrough that can reduce initial investment and maintenance costs.

“Digitizing every farm is a fantasy”… ‘realist’ arguments such as activating outsourcing services also raised
A hard-headed realist view was also raised that bringing all small farms into the digital sphere would have low effectiveness relative to the enormous cost.

One expert, who asked to remain anonymous, said, “Not every farm needs to be digitized, and it is questionable whether very small farms really need to adopt expensive AI,” arguing that support should be limited, as a matter of policy consideration, to subscription services or rental equipment. The expert further noted, “To secure the international competitiveness of our agriculture, consolidation into larger-scale farming is inevitable in the long run, and trying to cover every small farm could lead to mutual ruin due to excessive costs.”

Children using smartphones instinctively and farmers controlling equipment in the field are entirely different matters. Realistically, to include small farmers, the most feasible alternative identified was to guide them to receive the benefits of AI farming at low cost through “regional farmwork outsourcing services” or “shared models,” even if they cannot directly handle AI equipment themselves.

Ultimately, to support these field-tailored changes, it appears that governance reorganization must come first, along with establishing a step-by-step, option-based package support system and innovating the personnel, budgets, and project planning systems of government organizations such as agricultural technology centers in line with the changing farm environment.