[Anniversary Special Survey] No ‘People’ to Run AI Machines… Buried in ‘Equipment Distribution,’ We Missed Workforce Development

[Korean Agricultural Technology Newspaper = Reporter Lee Hyun-woo] The government is going all out to distribute advanced smart agricultural machinery equipped with artificial intelligence (AI) and robots, saying it will solve rural aging and labor shortages. However, concerns are growing in the field that expensive equipment could become useless because there is a shortage of the ‘high-skilled digital talent’ needed to operate and manage it.

To mark its founding anniversary, the Korean Agricultural Technology Newspaper conducted a survey of field experts, including steering committee members of the Korean Society for Agricultural Machinery and contributors to this paper. Experts spoke with one voice, saying that to foster ‘high-skilled digital talent,’ it is necessary first to systematize an education system for practice-oriented talent who understand both the field and technology, and to carry out a full-scale structural reform of public extension organizations.

“Away with classroom theory”… Calls for a complete overhaul of the education system centered on field practice
Survey participants agreed that current workforce development policies are greatly disconnected from the field. They pointed out that the paradigm must shift away from policies that simply distribute equipment and toward a ‘practice-oriented education system’ in which farmers and operators can directly read data and control equipment.

The most urgent improvement identified by experts was the establishment of field-practice-centered educational infrastructure organically linking universities, Agricultural Technology Centers, and companies. They said specialized educational courses based on agricultural machinery engineering should be strengthened around flagship national universities, and convergent talent combining AI, data, and agricultural machinery should be cultivated. Along with this, the introduction of a ‘Digital Agriculture Professional Engineer’ certification system to officially recognize field practical capabilities was presented as a concrete alternative.

A systematic approach to subdividing workforce development targets by job function was also proposed. One expert among the survey participants argued for a regional structuring of the education system, saying, “Workforce development must not be skewed only toward R&D; we need to design an ecosystem that encompasses field AS and manufacturing,” and suggested, “Specialized high schools should take charge of manufacturing, universities should train digital AS personnel, and graduate schools should train R&D research personnel, with these roles clearly divided and matched by region for operation.”

There were also strong calls to support trainees in completing both digital skilled-worker courses and cultivation technology courses at Smart Farm Innovation Valleys in an integrated manner. In the same context, it was also pointed out that training on immediate response measures for hardware and software failures should be included in regular curricula.

Professor Kim Yong-joo of Chungnam National University said, “A reorganization of academic departments and systems is needed so that departments such as computer engineering and agricultural machinery can be integrated,” adding, “At present, many cases are operated temporarily in the form of TFs and disappear when the project ends, but only by converting them into a sustainable national project system can we respond to the future.”

Professor Cho Yong-jin of Jeonbuk National University likewise advised, “To expand AI-based agriculture, it is essential to cultivate high-skilled digital talent who can understand both the field and technology,” adding, “Specialized educational courses should be strengthened around flagship national universities, and practice-oriented talent converged across agriculture as a whole, including AI and data, should be systematically fostered.” He continued, “We must expand field practice and industry-academia-research cooperation to build a professional workforce system that can operate in actual field settings.”

Agricultural Technology Centers must be transformed into ‘data prescription organizations’
Criticism was also raised that, for advanced agricultural AI technology to take root in the field, the roles of municipal and county Agricultural Technology Centers and rural extension officers—who communicate most closely with farmers—must be redefined with an engineering focus. This is because traditional farming guidance methods, such as variety selection or fertilizer prescription, are no longer sufficient to handle the flood of digital data and new technologies.

Experts diagnosed that ‘innovative reform’ is essential to revitalize Agricultural Technology Centers and strengthen the digital capabilities of rural extension officers. There were also calls to completely redesign the public workforce and budget of Agricultural Technology Centers around project planning and the development of field-oriented projects.

One expert who requested anonymity emphasized job innovation in extension organizations, saying, “The traditional agricultural technology guidance work of Agricultural Technology Centers must now change completely,” and stated, “While crop knowledge should remain the basic foundation, the job system of the public extension system must be changed so that staff can understand field data, analyze it on a statistical basis, and even provide prescriptions.”

“We must overcome the wall of preference for cities”… Improving settlement conditions for young operators is a direct hit
No matter how excellent an education system is created, if the talent that must actually run the technology does not flow into rural sites, the policy will amount to nothing more than empty words. Experts bluntly pointed to ‘local settlement conditions’ and ‘emotional limitations’ as the Achilles’ heel of workforce development.

At present, many assess that the legal and institutional foundations themselves for fostering talent, such as the Smart Agriculture Act, are in principle well established. However, the harsh reality is that the emotional barrier created by the overwhelming preference of young people for urban living has not been overcome. This is why strategies to increase youth inflow through an industry-academia-research field convergence ecosystem must be designed with greater precision.

To solve this, experts suggest that strong economic and environmental incentives must be combined with educational support. They explain that, in order to foster young farmers, successor farmers, and specialized smart agriculture operators and supply them stably to rural sites, agricultural AI technology companies should be encouraged to locate near farms. The dominant view is that wage support for company employees and policies to improve settlement conditions must be supplemented as a package.

In addition, since this is still an early-stage market in which users find it difficult to respond on their own, realistic support in which the government subsidizes costs so that product and service providers can advance their service systems and provide constant support to field users was also presented as an urgent task.