context: The 2026 World AI Conference showed that agricultural AI is moving from experimental technology towards real-world applications. Although the event did not include a dedicated agricultural AI exhibition area, AI technologies were already being applied across robotics, smart farming equipment and agricultural models. The sector still faces challenges including high costs, limited data and a shortage of cross-disciplinary talent. Future development will depend on making AI more accessible and practical for farmers.
The 2026 WAIC (World AI Conference) was held in Shanghai, bringing together more than 1,000 technology companies to showcase AI applications. Chinese Academy of Sciences academician E Weinan said that WAIC should become an important platform for presenting the PRC’s voice in AI, including agricultural AI. The PRC has developed strong capabilities in areas such as agricultural drones, smart farming equipment and agricultural robotics. The key challenge now is how to move these technologies from exhibitions and laboratories into everyday agricultural production.
Although agricultural AI did not have a dedicated exhibition area at WAIC, many AI applications demonstrated their potential in agriculture. For example, quadruped robots developed by DEEP Robotics have been explored for agricultural uses such as transporting fruit in orchards. Flexible robotic hands developed by Shanghai Jinjimiao Suan are being tested for tasks such as blueberry sorting, helping improve quality control for high-value agricultural products. These examples show that more AI companies are beginning to enter agriculture by solving practical production problems.
Agricultural AI is also moving from individual applications towards wider industrial integration. MaiMai Technology Group, an agricultural AI company, has combined agricultural data, AI models and robots to develop solutions for inspection, harvesting and sorting. Zhu Qingwei, the company’s business director, said the sector still faces several challenges, including a shortage of professionals who understand both agriculture and AI, difficulties in collecting agricultural data, and differences between farming environments in different regions. However, as agriculture requires higher levels of precision and standardisation, the long-term potential of agricultural AI remains significant.
Current applications already cover multiple stages of the agricultural value chain, including breeding, planting, livestock management, processing and farm decision-making. In crop production, smart machinery, drones and agricultural AI models can improve efficiency and reduce reliance on manual labour. In livestock farming, intelligent monitoring systems can support animal health management. In post-harvest processes, AI-powered sorting, storage and supply chain systems can improve product quality and standardisation. Compared with traditional farming methods that rely heavily on experience, AI is helping agriculture become more precise and data-driven.
However, large-scale adoption still requires several barriers to be addressed. Smart equipment and AI systems often involve high upfront costs, making adoption difficult for many small farmers. Rural areas also lack sufficient talent that combines agricultural knowledge with digital skills. In addition, agricultural production varies greatly across regions and crops, making it difficult to apply the same AI solutions everywhere. Future progress will depend on reducing the cost of adoption through agricultural services, policy support and stronger industry cooperation.
Overall, WAIC highlighted a new direction for agricultural AI development in the PRC. The future competition will depend less on technological demonstrations and more on whether AI can solve real problems faced by farmers. As technologies mature and application models improve, AI is expected to move from the 'cloud' to the field, becoming an important driver of agricultural modernisation.