“Tractors Costing Hundreds of Millions of Won Take at Least 7 Years Just to Break Even”… Advanced AgTech Makes Farmers Hesitate to Choose It

While the government is putting “agricultural digital transformation” and the “deployment of agricultural AI (artificial intelligence)” at the forefront as a master key to addressing rural labor shortages and the climate crisis, the actual state of deployment and technological maturity perceived by field experts has turned out to be close to a failing grade. Critics point out that compared with the dazzling pace of technological advancement, prohibitively high equipment prices, frequent sensor errors, and an almost nonexistent software after-sales service system are becoming massive barriers to farm adoption. To mark its launch, Korea Agricultural Technology News conducted a survey of steering committee members of the Korean Society for Agricultural Machinery and Engineering and this paper’s contributors. A total of 36 people participated in the survey, and the experts’ responses were analyzed using the AI assistant Jaeminai. 
 

Field deployment rate and AS satisfaction rank at the bottom
▲Why is perceived field deployment so markedly low?=The survey results, conducted mainly among executives of the Korean Society for Agricultural Machinery and Engineering, the country’s top authority group in agricultural mechanization and smart agriculture, were unsparing. On the question asking about “the actual level of field deployment of domestic physical AI and smart agriculture technologies,” experts gave an average score of only 3.53 points (1 = very low to 10 = very high). Of the 36 respondents, 20 gave scores of 3 or below (9 gave 3 points, 8 gave 2 points, and 3 gave 1 point). Only two respondents assigned a score of 7 or higher, which could be interpreted as field deployment having surpassed a certain level.

As the decisive reasons for the slow rollout (up to three choices), experts most frequently selected “the burden of initial purchase cost (66.7%, 24 responses).” This was followed jointly in second place by “the performance gap between the laboratory and the field (41.7%, 15 responses)” and “Korea’s unique environment of small-scale and irregularly shaped plots (41.7%, 15 responses).” Low compatibility between devices (25%, 9 responses), the absence of a field-oriented coaching system (25%, 9 responses), unstable AS and repair convenience (22.2%, 8 responses), and the difficulty elderly farmers have in operating the equipment (19.4%, 7 responses) followed behind.

This can be interpreted to mean that in a situation where farm income per pyeong has stagnated, the ROI (return on investment) does not justify purchasing advanced equipment costing hundreds of millions of won, and that cutting-edge technology is failing to take root because of domestic farming conditions centered on fragmented farmland. In the same context, “expanding cultivated land area” (47.8%, 22 responses) received the most selections in response to the question asking what prerequisite must be resolved first for the introduction of agtech such as AI to lead to a real increase in farmers’ net profits (up to two choices). “A dramatic reduction in device prices” also accounted for 26.1% (12 responses).
 

Professor Ha Yoo-shin of Kyungpook National University explained, “Our country’s technological level has risen to be on par with advanced nations. However, it is true that because of the many variables in the field, there is a gap between technology development and actual adoption,” adding, “Farmers also want ‘practical technology they can use right now’ rather than futuristic cutting-edge technology.”

In particular, the responsiveness of the after-sales service (AS) system for data errors or software failures was only 3.58 points out of 10. Unreliable after-sales management can become a reason farmers hesitate to purchase advanced equipment.

Among cases of AI malfunction occurring in the field, the most frequent and fatal type by far was “sensor recognition errors (61.1%),” followed by “communication outages and signal interference (38.9%).”

Unlike standardized ordinary roads, farmland is full of variables such as vibrations from unpaved surfaces, dust, and strong sunlight reflection. For this reason, the “reliability of sudden obstacle avoidance algorithms” that identify crouching farmers or animals was rated 5.94 points, and the “ability to diagnose pests and diseases” that detects subtle changes in the early growth stage was rated 5.64 points, leading to the assessment that these technologies have still not fully moved beyond the laboratory level.

One academic society expert who responded to the survey conveyed the voice from the field, saying, “At present, the domestic AS infrastructure is geared only toward repairing mechanical failures in tractors or rice transplanters,” and “when digital failures occur, such as software firmware conflicts, cloud linkage failures, or missing training data, it is not uncommon for work to be halted for days because the cause cannot even be identified on-site.” 

BEP (break-even point) expected to take 6.85 years just to achieve
If the time needed to recover the funds invested in purchasing equipment were short, the pace of field deployment could accelerate. But under domestic agricultural conditions, even this does not seem easy. To the question, “Excluding subsidy benefits, how many years do you predict it will take to recover the equipment price (reach BEP) purely through improved work efficiency?” respondents (29 people) projected an average of 6.85 years.
 

Participants who answered 10 years or more assumed the purchase of advanced, large-scale equipment such as autonomous tractors costing 150 million won to nearly 300 million won and unmanned harvesting robots, while more than half of the experts expected that if general smart electric equipment or complex control systems such as autonomous rice transplanters were introduced, the break-even point would be formed in about 5 to 7 years.

Experts explained that although “it is not easy to present a uniform figure because there are many variables depending on the environment, such as the equipment purchased and the size of farmland,” the recovery period is bound to be long given the characteristics of agriculture, where use is concentrated in specific cropping seasons or times of year, as well as the domestic agricultural environment of high production costs and low income.

In particular, it is hard not to judge 6.85 years as a very high-risk investment period for farms expecting only improved work efficiency without government subsidies, and this could become an obstacle to the spread of AI and smart agriculture.

Professor Cho Yong-jin of Jeonbuk National University advised, “Farmers can accept a certain level of price premium, but if it exceeds 1.5 times the current equipment price, purchase intention may decline. Therefore, to expand deployment, it is important not simply to support prices but to focus on improving work efficiency, ease of use, and maintenance reliability, and to establish a stable operation and management system.”

It is also regrettable that progress has been insufficient in creating a digital environment in which advanced technology can be grafted onto farmland. Professor Kim Yong-joo of Chungnam National University said, “Most domestic farmland is designed with humans at the center,” adding, “We need to create environments where robots can work. For example, if a robot work environment is established in orchards, output per unit area may decrease, but quality can improve. A paradigm shift is needed in this way.” He continued, “To get farms to participate, we need to show successful cases. Also, participating farms need to be offered another benefit for motivation.”

#What is the solution?
The key task in combining agriculture and AI is securing high-quality data and standardizing facilities and equipment
As the most critical task for successfully combining agriculture with agtech such as AI, experts cited securing high-quality data and standardization as the top priorities. In fact, in response to this question, 41.7% of respondents (15 people) selected “securing high-quality field-based data and standardizing facilities and equipment.” Experts strongly warned that no matter how excellent an artificial intelligence algorithm or model may be, if the field data being input is insufficient or biased, it will inevitably produce incorrect predictions and misdiagnoses.

In particular, they explained that only by systematically accumulating “full-cycle agricultural data” ranging from soil management to sowing, cultivation, pest control, harvesting, and distribution can the volatility of climate and terrain in open-field environments be overcome.

Professor Ha Yoo-shin of Kyungpook National University underscored the importance of data infrastructure, saying, “Among the key tasks of agricultural AI, the most foundational is high-quality data,” and “without data, AI development, deployment, verification, and even government policy are all impossible.”

Professor Kim Yong-joo of Chungnam National University said, “We need to understand full-cycle agricultural data in order to decide what to do, but it is difficult because data from each stage is not connected,” emphasizing the need for systematic data acquisition. Another expert also presented a specific alternative, saying that the prompt deployment of a “GPS-based yield monitoring system,” which would serve as target data, is urgently needed to activate basic data collection and analysis.
 

Standardization of facilities and equipment is also urgent. Professor Yang Myung-gyun of Jeonbuk National University argued, “National standardization work is absolutely necessary, but as domestic companies stick to proprietary technologies, securing compatibility is not easy. The government must take the lead in resolving the issue of technology standardization.” Professor Ha Yoo-shin of Kyungpook National University also mentioned the need for standardization, saying, “Without standardization, it is not easy for companies to develop modules and the like.”

The second task is “innovation in government governance and the establishment of an industry-academia-research cooperation system (27.8%, 10 people).” Experts called for an urgent paradigm shift that links agriculture and agtech together. Professor Jeong Seon-woo of Chonnam National University said, “If things are led mainly by data experts who do not understand crop physiology well, accurate data cannot be secured. That is why we need a cooperative system through the convergence of experts from each field.”

Shift to a ‘shared-use’ deployment model centered on agricultural machinery rental centers… expanding government support is also urgent
Experts demanded that “expanding cultivated area (47.8%)” and “a dramatic reduction in device prices (26.1%)” are necessary prerequisites for agricultural AI to lead to a real increase in farms’ net profits. This is based on the unsparing judgment that with the average cultivated area of Korean farms (around 1 hectare) and low equipment utilization rates, it would take at least 10 to 15 years or more to recover equipment costs without subsidies—or it may be impossible altogether.

Accordingly, the dominant view is that instead of the current approach of effectively forcing each individual farmer to buy expensive machines, the policy paradigm should make a sharp turn toward a “shared-use (rental-type) deployment model” centered on agricultural machinery rental centers or regional field management organizations. The suggestion is also gaining traction that effectiveness will be higher if support is diversified from hardware purchase support for creating an initial boom to long-term support for “service (subscription-type software) usage fees.”

Professor Jeong Seon-woo of Chonnam National University said, “Considering smooth management and maintenance, I think a shared-use deployment method through agricultural machinery rental centers and the like is more suitable.”

A proposal was also made to operate a two-track approach depending on farm size and the state of digital environment development. Professor Cho Yong-jin of Jeonbuk National University emphasized, “We need a system that manages and supervises in a unified way based on an integrated platform, running in parallel individual deployment centered on large farms and agricultural machinery rental deployment centered on small and medium-sized farms, while centralizing equipment operation, safety, maintenance, and data management.”

Professor Ha argued, “Future deployment strategies must be thoroughly dual-tracked. For items with high field acceptance, such as rice cultivation, they should be spread quickly through customized support systems, while for items that are difficult to apply in the field, the gaps should be filled through demonstration-centered research.”

There were also calls for expanded government budget support. Professor Yang Myung-gyun of Jeonbuk National University said, “The gap between developed technology and field deployment is very large. Investment must be made not only in short-term results or immediately necessary research, but also in future-oriented and original research. Support and investment are needed from a long-term perspective to lead agricultural development. Realistically, expanding subsidy support for the time being is also one way to reduce the purchase burden on farms and raise deployment rates.”

Need to improve the expertise of policy authorities and build an industry-academia-research cooperation ecosystem
Harsh criticism also emerged regarding the lack of expertise among the bureaucrats who make policy. One respondent who requested anonymity bluntly said, “The people drafting agricultural AI policy do not accurately understand AI,” adding, “Policy should be drafted by people who accurately understand actual field work and technology.”

The complacent attitude and small scale of the domestic agricultural machinery industry also came under fire. A professor at one university said, “Even among major domestic agricultural machinery companies, there are many cases where they are not interested in or prepared for developing new technologies,” adding, “To go beyond merely using the shell of artificial intelligence and acquire deep technological capabilities, a deep collaborative research ecosystem with universities or specialized research institutes—not companies acting alone—is essential.” In addition, the need to establish a pan-governmental AI committee to comprehensively manage laws and institutions as well as R&D was strongly raised.

In addition, the “urgency of establishing legal guidelines” to determine responsibility among manufacturers, developers, and farms in the event of accidents caused by AI malfunctions scored a very high 8.06 points. This is because if there are no standards of responsibility for autonomous agricultural machinery deviating from its route or drone malfunctions, it becomes impossible to design insurance products, which in turn becomes a boomerang that ultimately dampens farms’ willingness to adopt and undermines manufacturers’ incentives to launch products.