Spring plowing season is underway, and a visit to the fields reveals a refreshing scene: watering robots, AI diagnostic models, and versatile drones are among the new high-tech farming tools bustling on the front lines of spring cultivation and management.
The 2026 Central Document No. 1 emphasized the need to "develop new quality agricultural productivity tailored to local conditions, promote the integration of artificial intelligence with agricultural development, and expand application scenarios for drones, the Internet of Things, and robots."
How are these "smart tools" taking root in the soil to support agricultural production? Let’s take a closer look.
**Irrigation Robots: Precision Watering for Wheat Fields**
Spring irrigation is crucial for stabilizing and increasing wheat yields. In a wheat field in Taikang, Henan, a large machine with an antenna moves steadily along the ridges. Behind it, a single spray nozzle showers a fine mist evenly over the winter wheat, which is in the jointing and booting stage.
This is the "Zhenlin No. 1," an intelligent unmanned irrigation vehicle developed by Zhengzhou Nongren Irrigation Technology Co., Ltd. It features autonomous navigation, automatic turning, and integrated water-fertilizer operation.
Weighing about two tons, this "steel giant" operates with remarkable precision. Relying on the BeiDou high-precision positioning system, it deviates less than two centimeters over 100 meters in a straight line and within five centimeters when turning.
Theoretically, the "Zhenlin No. 1" can irrigate over 400 acres per day, but the manufacturer recommends 100 to 150 acres. Why the discrepancy? "Smart irrigation is about calculating water usage," explains Zhang Xinxi, head of Henan Nongren Irrigation Equipment Co., Ltd. "During the green-up stage, wheat roots are highly active, and soil moisture must reach a depth of 20 to 30 centimeters. If the robot moves too quickly and only wets the surface to less than three centimeters, it’s ineffective. Slowing down to ensure each acre is thoroughly watered is both economical and scientific."
Its endurance is equally impressive. With a hybrid oil-electric range-extending power system and lithium iron phosphate battery pack, it can operate continuously for over 100 hours. Data on location, speed, water volume, and operational status are transmitted in real-time via the Internet of Things, allowing farmers to monitor and manage the system remotely.
Ma Jianqin, Vice President of North China University of Water Resources and Electric Power and technical advisor for the project, notes that smart irrigation robots address the urgent need for efficient water use in agriculture. During the "14th Five-Year Plan" period, China added over 53 million acres of irrigated farmland, bringing the total to more than 1.09 billion acres—an irrigation rate of 56.2%. Yields in large and medium-sized irrigation districts are 1.5 times the national average and 2.7 times that of drylands.
"Water is the lifeblood of agriculture, and conservation is key," Ma emphasizes. Traditional flood irrigation results in significant waste, while smart robots enable precise watering, multiplying water-use efficiency. This shift from "flooding fields" to "precision hydration" reflects the deep integration of digital technology and agricultural production—a vital step toward "storing grain in technology."
**Specialized AI Models: Giving Farmland a "Smart Brain"**
Questions like "Should winter wheat be fertilized during green-up? How much water is needed at jointing? What causes spots on leaves?" used to rely on farmers' experience. Now, they have a new assistant: Shennong Model 3.0, developed by China Agricultural University.
This model is one of the most comprehensive vertical AI systems in the country, covering 90% of agricultural disciplines and 80% of farming scenarios. It incorporates six categories of 36 specialized agents for smart planting, breeding, livestock management, remote sensing, and weather services, serving over 100,000 farmers nationwide.
"General AI models are knowledgeable but not specialized enough for agriculture," explains Wang Yaojun, an associate professor at China Agricultural University who leads the Shennong Model team. "Ask a general AI when to water wheat, and it may give a vague answer. But farming is too complex for one-size-fits-all solutions."
To make AI truly understand farmland, the team built a knowledge base with 10 million agricultural knowledge graphs, 20 million labeled images, and 50 million production records. Using dynamic sparsity and incremental compression techniques, the latest version uses 50% less computing power while improving performance on key tasks by 5%.
If Shennong Model is a knowledgeable "agricultural consultant," its 36 agents are specialized "doctors." For example, the "Shennong Field Guard" plant protection agent achieves over 95% accuracy in identifying pests and diseases. More importantly, it combines weather and soil data to predict disease spread, providing 7- to 10-day early warnings. The model is now open for collaboration with plant protection agencies nationwide.
At the Shennong AI Farm in Huairou, Beijing, sensors in the soil monitor over 20 parameters, including moisture and nutrients. Data fed into the Shennong Model enables customized irrigation and fertilization plans via the "Shennong Water-Fertilizer Decision Agent," boosting water-fertilizer efficiency by over 30% and reducing water use by 10%. In demonstration bases like Fujin, Heilongjiang, the system has cut chemical fertilizer and pesticide use by 10–15%, lowering costs while protecting the ecosystem.
With AI and other advanced technologies, traditional farming—once at the mercy of the weather—is evolving into a practice of "working with foresight."
**Aerial Farmhands: Drones for Spraying, Scouting, and Transport**
If robots are the "ground players" and AI models the "unseen advisors," then agricultural drones are the agile "aerial farmhands" of spring plowing.
Civil aviation data show that agriculture accounts for 98% of drone flight hours in China. DJI Agriculture, a leader in the field, reported that in 2025, over 335,000 of its agricultural drones operated domestically, completing 3.3 billion acres of work and transporting 6.5 million tons of materials.
In spring, drones first prove their worth in crop management. Winter wheat requires fertilization, pesticide application, and disease prevention during green-up and jointing; rapeseed, fruit trees, and vegetables also need timely plant protection. Drones enable rapid deployment, precise spraying, and reduced human exposure to chemicals.
In many plains, drones have become standard for spring management: one day they scout fields, the next they apply pesticides or fertilizers according to prescription maps. For farmers, they function as a "sharable" platform—service providers can deploy them on demand.
Drones also excel in transport, especially in hilly areas. During spring orchard management, carrying fertilizers, pesticides, and tools up and down slopes is labor-intensive. With mature lifting capabilities, drones can not only "carry fruit downhill" but also "deliver supplies uphill," turning backbreaking tasks into low-altitude logistics.
Cheng Zhongyi, a senior solutions engineer at DJI Agriculture, envisions the next phase focusing on "lowering the barrier to use" and "expanding smart farming applications." This means making drones easier to operate while integrating them with data collection and decision-support systems to serve the entire farming cycle—from plowing and planting to management and harvesting.
From irrigation robots on the ground, AI models in the cloud, to drones in the sky—this spring, the vast fields resonate with a symphony of digital code and advanced manufacturing. The seeds of new quality agricultural productivity are taking root in the spring breeze.
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