Over wheat fields, drones fly low, instantly marking nitrogen-deficient seedlings or aphid infestations on a screen in red. Inside greenhouses, Artificial Intelligence algorithms keep watch, alerting the system immediately when water or fertilizer is lacking. In sorting workshops, AI vision systems examine hundreds of apples per second, grading them by color, size, and blemishes with a misjudgment rate of less than one percent. Shifting from "relying on the weather" to "knowing the weather to act," Artificial Intelligence is becoming a key tool for stabilizing agricultural output, improving quality, and boosting farmers' incomes, offering a new paradigm for agricultural and rural modernization.
The 2026 Central Document No. 1 explicitly called for "developing new agricultural productive forces tailored to local conditions and promoting the integration of Artificial Intelligence with agricultural development," upgrading AI technology from single-point applications to a strategic engine spanning the entire agricultural industry chain. Multiple industry institutions predict that China's smart agriculture market will exceed 134 billion yuan in 2026, maintaining a compound annual growth rate of over 15%. The recently concluded 11th Plenary Session of the 12th Provincial Party Committee, focusing on transforming agriculture into a modernized large-scale industry and promoting digital and intelligent transformation, clearly proposed implementing the "AI Plus Agriculture" initiative.
What is "AI Plus Agriculture"?
In short, it is a new production system driven by Artificial Intelligence technology, guiding traditional agriculture towards intelligent and efficient transformation. Attendees at the meeting believed that, on a deeper level, "AI Plus Agriculture" is not a simple technological overlay but a systematic reshaping of agricultural production methods, organizational forms, and value chains.
AI Farming Unleashes Greater Potential in Fields
The vitality of technology lies in solving practical problems. Nationwide, agriculture faces pressures such as an aging labor force and increased risks from climate change, along with issues like low added value of specialty products and weak brand strength. The introduction of Artificial Intelligence is pushing agriculture from "relying on experience" to "relying on data," injecting certainty into securing China's food supply and strengthening the agricultural industry. Many see the application and promotion of Artificial Intelligence as timely.
"Shandong is a major agricultural province and the birthplace of agricultural industrialization, with a large industrial scale, diverse business formats, and a high-quality workforce open to new things. At the same time, Shandong has a strong industrial foundation and rapid development in Artificial Intelligence technology. The transformation and promotion of new technologies in agriculture have significant cost and efficiency advantages." Liu Yue, a professor at the Shandong Provincial Party School (Shandong Academy of Governance) and Dean of the Shandong Rural Revitalization Research Institute, believes Shandong has a solid foundation for developing "AI Plus Agriculture." With advantages such as a complete industrial system, considerable market scale, and rich application scenarios, the fields will unleash greater potential.
On the summer plains of northern Shandong, corn is growing strong, full of vitality. In the demonstration field of the Jinsui Grain Planting Professional Cooperative in Qihe County, a network of black pipes plays a crucial role. "This is our integrated water and fertilizer system. There is a water outlet every 30 centimeters, allowing precise drip irrigation to the bottom of the corn roots. Compared to flood irrigation, it is three times faster, saves 30% water, and one person can manage 200 acres per day," said Yuan Bengang, the cooperative's director. Relying on a big data platform, the cooperative can also implement full-process supervision in the "cloud," creating customized water and fertilizer management plans for different plots with a few clicks, developing personalized base fertilizer formulas for each village's corn fields, and simultaneously providing services like soil testing and crop chlorophyll detection.
This year, Shandong achieved a bumper summer grain harvest against the trend, with increases in area, yield, and total output for four consecutive years. This is inseparable from the promotion of integrated water and fertilizer technology in large fields. Data shows that in 2025, the area of grain and oil crops applying this technology in the province reached 6.3 million mu. "The combination of integrated water and fertilizer with ridge reduction and land expansion, as well as wide and narrow row dense planting, accelerates the deep integration of improved seeds, methods, machinery, and soil, effectively enhancing the large-scale and standardized planting level of farmland," Yuan Bengang deeply felt. The implementation of integrated water and fertilizer has given the harvest a more solid foundation.
Seeds are the "chip" of agriculture and key to ensuring food security. Intelligent breeding has become a hot topic for breeding researchers. In April this year, the "iWheat Wheat Intelligent Design Breeding Platform," jointly developed by the Institute of Crop Sciences of the Chinese Academy of Agricultural Sciences and Shandong Jizhi Biotechnology Co., Ltd., was launched. For the wheat genetics and breeding team at Qingdao Agricultural University, the application of iWheat is helping to overcome industry challenges like "long cycles, low efficiency, and high costs" in wheat breeding. This platform integrates the genotypes of over 30,000 wheat germplasm resources, 442 functional genes, over 6,000 functional loci, and more than 400,000 phenotypic data records, establishing a "digital ID" for each germplasm, enabling precise retrieval and efficient use of resources. "Combined with a rapid breeding system, it can compress the traditional 8-year breeding cycle by 2 to 3 years," said team leader Zhang Yumei. Accelerating the cultivation of new varieties and reducing breeding costs means enhancing the innovation capacity and competitiveness of the entire seed industry, thereby empowering the high-quality development of the wheat industry chain upstream and downstream.
Turning "Technological Showpieces" into "Industrial Landscapes"
Agricultural large models can integrate multi-dimensional data such as weather, soil, and crop growth, significantly improving the precision and predictability of agricultural production. The province has clearly stated its intention to strengthen the application of agricultural large models and AI agents. In Shouguang, known as the "Hometown of Chinese Vegetables," vegetable greenhouses have welcomed a group of special "employees." Agile robot dogs move flexibly along the ridges, their high-definition cameras and sensors acting like "fire eyes," accurately identifying signs of pests and diseases. After the inspection, data is transmitted back to the backend in real-time, and the Artificial Intelligence model quickly provides planting decisions. Today, newly built greenhouses in Shouguang have been upgraded to the seventh generation of intelligent IoT "cloud greenhouses," with an IoT application rate exceeding 85% and labor productivity doubled compared to the past.
Just over a month ago, the Shandong Academy of Agricultural Sciences released the "Shun Geng · Liang An" agricultural remote sensing large model. Liang Jinguang, Party Secretary of the academy, explained that the model is based on 20 years of accumulated massive data from 20 million manual samples, covering all landforms and phenological cycles across Shandong. It has built a full-chain intelligent monitoring system covering "intelligent cropland extraction, soil fertility and moisture prediction, precise crop identification, dynamic growth monitoring, pest and disease warning, and intelligent crop yield estimation," with an accuracy of up to 95% and efficiency improved by over 100%.
In the midstream processing stage, large models can also effectively enhance product added value and standardization levels. In Laishan District, Yantai, the "Laishan Grape Cultivation and Winemaking Large Model," jointly created by Inspur Cloud's subsidiary Shuyun Nong (Shandong) Information Technology Co., Ltd., the Computing Center of China Agricultural University, and Yantai Puxiang Agricultural Development Co., Ltd., automatically generates climate-adaptive regulation plans on the planting side, achieving precise water and fertilizer application and intelligent pest and disease warning and control, simultaneously improving grape yield and quality. On the winemaking side, it uses AI to simulate the entire fermentation and aging process, dynamically optimizing key parameters and completing digital modeling of raw material grading, fermentation control, and wine flavor profiles. This makes the taste of each batch of wine more stable and quality more controllable.
Of course, achieving a deeper two-way empowerment between Artificial Intelligence and the vast rural areas still has a long way to go. An AI system, including sensing terminals, edge computing, and decision models, requires a significant initial investment. Can ordinary farmers afford and use it effectively? Agricultural production has long cycles, complex environments, and large regional differences. How can high-quality agricultural data be collected and utilized to drive AI model iteration? There is a shortage of young talent in villages who understand both technology and agriculture. How can AI agricultural technology talent be attracted and cultivated?
To truly turn "technological showpieces" into "industrial landscapes," Liu Yue believes the key is to encourage leading enterprises, universities, research institutes, and new agricultural business entities to collaborate. Focusing on advantageous industries like grains, fruits and vegetables, animal husbandry, and aquaculture, they should create a batch of industry-specific large models that "understand farming, calculate accounts, and manage the entire process." Additionally, Liu Yue suggested paying more attention to building new rural production relations that are compatible with new rural productive forces. This involves consolidating and activating various rural assets and resources to provide guarantees for fully leveraging the role of digital and intelligent technological progress. "Promoting the transformation of rural digitalization from a 'local area network' to an 'internet' requires breaking down the long-standing 'information islands' and building a data foundation for rural revitalization."