“Productivity Jumps 38% Through Human-Robot Collaboration”… The World Is Now Shifting Into the Era of Agricultural Robots

[Korea Agricultural Technology Newspaper = Reporter Baek Cheol-hyun] Leading scholars from Europe, the United States, China, and Japan, which are spearheading the global agricultural robot market, gathered in one place to share the current state and field-verified achievements of advanced AgTech. The Korean Society for Precision Agriculture held the ‘2026 Spring Academic Conference’ in connection with the Agricultural Technology Expo held at Cheongju OSCO on June 19, and intensively explored the path forward for Korean agriculture. 

Europe (EU): Beyond standalone robots, a ‘data ecosystem’ and powerful field validation (TEF) infrastructure

According to a presentation by Senior Researcher Han Chang-ho of the Europe Research Institute at the Korea Institute of Science and Technology (KIST), Europe is approaching agricultural robots not as a single technology but as a vast data ecosystem. At the data collection stage, satellites covering wide areas, drones (UAVs) capable of high-resolution mapping, and tractor-mounted sensors that closely mark individual crops are mobilized. More recently, an ultra-sensitive ‘photoelectron emission sensor’ capable of detecting changes in potato emission levels caused by minute physiological changes in crop leaves has even been developed, bringing the field to the stage of locally precise diagnosis of crop health.
The data collected in this way leads, through AI systems, to crop growth diagnosis and productivity forecasting, and ultimately robots and smart agricultural machinery take real actions such as precision spraying, weeding, and harvesting. A representative example is the grape harvesting robot, a key technology in Europe’s wine industry. According to the latest research 발표ed last year, when a ‘diagnostic robot’ equipped with a visual imaging system first analyzes the location and ripeness of grape clusters with a hyperspectral camera, a ‘harvesting robot’ fitted with two robotic arms (a gripper arm and a cutter arm) is then deployed, operating an organic collaborative system that selects only high-value grapes and cuts their stems.
The biggest point at which Europe’s agricultural robot ecosystem differs from Korea lies in its strong government-level ‘field validation infrastructure.’ Noting that highly advanced prototypes in laboratories (TRL level 6) have difficulty entering the actual market (TRL level 8) directly, the EU is operating ‘agrifoodTEF,’ an official platform that serves as a highway connecting the two. This platform makes the ‘Real-world Testing’ stage mandatory, directly confronting variables that cannot be perfectly controlled in a laboratory, such as climate, soil, and unexpected worker behavior. Only technologies that secure reliability by passing thorough field verification are recognized as solutions needed in the market, and costs are also eased for farms and companies through the EU’s systematic support measures.

China: Intelligent agricultural machinery growing explosively at 78% annually… a ‘Warring States era of robots’ across all sectors

Professor Han Woong-cheol of Kangwon National University, who presented on trends in China’s robot agriculture market, said, “The intelligent agricultural machinery market is showing very rapid growth, recording an annual growth rate of 78%,” adding, “Recently, the robot industry has also been growing rapidly, and as humanoid robots and multimodal-based robots are combined with large language models (LLMs), robot capabilities are improving significantly.”
Currently in China, because the technologies required differ by field—such as open-field farming, orchards, greenhouses, livestock, and fish farms—a variety of robots are being developed for each area, and the pace of technological development is also extremely steep. In particular, among open-field agricultural robots, the unmanned rice weeding robot automatically recognizes rice stalks and extracts a central guide line to analyze its route on its own. Its recognition rate reaches 94%, and it is also equipped with a work capability of 13.8 m/s and obstacle avoidance functions.
As befits the world’s largest tea-producing country, a mobile sensing T-Bot specialized for tea fields is also drawing attention. Posture control technology has been applied as a basic feature to suit cultivation environments with many slopes. In addition, a tea harvesting robot has achieved a 90% harvesting rate by applying a solar power system and can harvest about 2,300 tea leaves per hour. In the fruit sector, a system integrating harvesting, collection, and transport into a single platform has entered the commercialization stage. An apple harvesting robot recorded an 82% success rate within the visible area, and recent research has evolved into a ‘multi-robot arm system’ that cooperates like a human using both hands. The integrated kiwi robot developed by Jiangsu University works with four robotic arms in parallel to harvest up to 3,600 kiwis per hour, a level that can replace 3 to 5 skilled workers.
In addition, the cotton topping robot developed by Duoatech demonstrated a 95% work success rate by incorporating night cameras and stereo vision technology. In particular, by operating 36 working arms simultaneously, it achieved work efficiency 50 times that of humans, presenting the possibility of large-scale farming. In the livestock and fisheries sectors, Raisense Robotics’ livestock monitoring robot automatically analyzes pigs’ body temperature, cries, and weight to detect disease early. Poultry farm robots use both visible-light and thermal imaging cameras to detect carcasses and check egg quality, and operate unmanned 24 hours a day with automatic charging functions. In the fisheries sector, a cleaning robot that cleans aquaculture nets through underwater autonomous driving has emerged, recording work efficiency 5 to 8 times higher than humans, while an unmanned feeding robot that supplies feed along a set route has also emerged as a key to labor reduction.

United States: ‘Customized AI’ and ‘digital twin’ solutions to overcome outdoor field constraints

Outdoor strawberry fields make AI recognition extremely difficult due to changes in sunlight, light reflection, and irregular structures. Professor Choi Da-eun of the University of Florida, who researched AI models for strawberry cultivation, explained the importance and difficulty of AI recognition that day, reviewed problems identified during field verification, and presented solutions to them.
Professor Choi implemented an algorithm applying a voting system for images in a natural enemy spraying system used to prevent pests in strawberry cultivation. The model itself showed an accuracy of 85%, and in situations where detection succeeded, it showed a precision of 95%. In addition, according to field verification results for a runner (stem) cutting robot, its ability to find runners hidden or covered by leaves remained limited, as about half of all runners were located in places difficult to identify visually, leaving the overall success rate at 48%. These two cases suggest that even if the hardware itself is excellent, accumulated AI recognition errors can have a fatal impact on the performance of the entire system.
To solve these problems, Professor Choi applied a digital twin-based virtual simulation solution, proving that the limitations and cost issues of field data collection can be dramatically overcome by utilizing a virtual reality ‘digital twin’ that exactly mimics the real environment and synthetic data. Professor Choi said, “When 10% real data is mixed with virtual data, the accuracy comes close to 100%.”
She also emphasized, “The final performance of strawberry automation robots is determined not by hardware but by AI recognition precision,” adding, “Digital twin technology, which can overcome the limitations of the natural environment and dramatically lower data collection costs, will become an essential strategic solution in the future AX (AI transformation) agricultural robot market.”

Japan: A breakthrough for the crisis of extinction from super-aging… ‘Human + robot collaboration’ improves productivity by 38.6%

Japan is currently in urgent need of improving the constitution of smart agriculture to respond to the disappearance of rural communities and super-aging. With 58.7% of Japan’s agricultural workers aged 70 or older and the core population expected to plunge to 300,000 by 2043, the country is carrying out a major national strategic transformation, including consolidating idle farmland and introducing unmanned agricultural machinery.
In particular, the ‘robot + human’ cooperation model is a key solution that makes on-site economic feasibility and labor force 확보 possible. Professor Ida of Kyoto University, who gave the lecture, said, “As a result of demonstrating ‘Level 2 (autonomous driving under nearby supervision)’ from the four-stage guidelines of Japan’s Ministry of Agriculture, Forestry and Fisheries (MAFF) in large-scale rice cultivation fields, when a person worked alone as before, it took 0.389 hours per 10a, but through collaboration between humans and robots, tillage work time was reduced to 0.239 hours, proving a clear on-site profitability indicator of a remarkable 38.6% reduction in work time.” In addition, Japan is presenting essential technological improvements by using deep learning-based object recognition (DDRNet) technology to precisely identify fallen rice and obstacles with more than 84% accuracy, and by integrating and standardizing real-time yield and protein content maps on a cloud platform (FMIS). Furthermore, technological development continues to build an information ecosystem that organically links remote sensing data and agricultural machinery operation data with cloud systems and IoT platforms to enable real-time integrated management of the entire farm.