Policy-Driven — The National Strategy for Smart Agriculture
In 2024, China's Ministry of Agriculture and Rural Affairs released the National Smart Agriculture Action Plan (2024-2028), explicitly calling for deep integration of AI, big data, and IoT with agriculture. This marks a new era of "agent-driven" agriculture, where AI agents — entities with autonomous perception, decision-making, and execution capabilities — move from labs to fields as the core engine of smart agriculture.
The plan targets a fully established smart agriculture system by 2028, featuring "omnipresent sensing, intelligent decision-making, precision operations, and networked services." Four major engineering initiatives were launched: agricultural sensor development, agricultural LLM research, intelligent machinery upgrades, and agricultural data sharing platforms.
The policy explicitly supports "agricultural LLM + multi-agent systems" joint research, encouraging collaborative development of open agricultural AI platforms across industry, academia, and research institutions.
Smart Agriculture System
Omnipresent sensing, intelligent decisions, precision operations
Key Initiatives
Sensors · LLMs · Smart Equipment · Data Platforms
Frontier Research — Multi-Agent LLMs in Agriculture
In Chinese academia, "multi-agent large language models" have become one of the most active research directions in agricultural AI. Since 2024, multiple CCF-A ranked international conference papers have explored applying LLM and multi-agent collaboration frameworks to agricultural scenarios. Research hotspots include agricultural knowledge graph-enhanced LLM reasoning, injecting structured knowledge such as crop models, soil data, and weather information into LLMs.
Knowledge-Enhanced Reasoning
Injects crop models, soil data, and meteorological information into LLMs to improve interpretability.
Collaborative Planning Framework
Designs specialized agent roles — "Crop Manager," "Soil Analyst," "Weather Forecaster" — that negotiate to complete complex tasks.
Instruction Fine-Tuning Dataset
Ten-million-scale Chinese agricultural instruction datasets based on crop cultivation standards.
Multimodal Agricultural Agents
Fuses drone imagery, field sensor data, and natural language instructions for real-time crop growth diagnosis.
Precision Agriculture — Drone Inspection & Intelligent Decision Loops
Precision agriculture is the domain with the densest deployment of AI agents. Centered on drone inspection, combined with multispectral sensors and AI reasoning agents, a complete closed loop of "Sense-Analyze-Decide-Act" has been established. DJI Agriculture's T-series crop protection drones can capture imagery of 3,000 acres daily.
AI agents analyze imaging data at the edge in real-time, identifying pest zones, nutrient-deficient areas, and weed distribution, automatically generating differentiated spray prescription maps. The entire cycle has been compressed from 3-5 days to under 2 hours.
Variable-Rate Fertilization
AI agents integrate soil sensor data, satellite remote sensing indices, and crop growth models to dynamically calculate N-P-K ratios. Fertilizer use reduced by 20-30%, yields increased by 10-15%.
Intelligent Pest Control
Agents analyze local weather data and pest outbreak models to accurately predict outbreak windows and autonomously trigger control operations.
Smart Farms, Ranches & Fisheries — Industry Benchmark Cases
Smart Rice Farm
Heilongjiang Jiansanjiang National Demonstration Zone deployed an agricultural IoT monitoring network covering 100,000 mu. AI agent center uses a digital twin platform for real-time crop monitoring. Rice yield reached 687 kg/mu, 23% above average, with 35% water and 42% pesticide savings.
Smart Ranch
At Inner Mongolia Yili Modern Smart Health Valley, each dairy cow wears a smart collar for health monitoring. AI agents predict estrus cycles and disease risks, adjusting feed robot formulations. Ranch efficiency improved by 35%, annual milk yield per cow up 12%.
Smart Fishery
Shandong marine ranches use ROVs and water quality sensor arrays, with AI agents constructing 3D water environment digital models to dynamically predict red tide and hypoxia risks, enabling deep-sea aquaculture producing 3,000 tons annually.
Agricultural Sensors & IoT — The Agent's Sensory System
The agent's "sensory system" consists of sensors and IoT terminals spread across farmland. As of 2025, over 12 million agricultural IoT terminal nodes have been deployed nationwide, forming a sensing network covering crop cultivation, livestock, aquaculture, and agricultural machinery.
These sensor data converge to the agricultural agent platform via LoRaWAN, NB-IoT, and 4G/5G protocols, forming a "device-edge-cloud" three-tier architecture. Edge agents handle millisecond-response events, while cloud agents manage global optimization and cross-region coordination.
Soil Sensors
Temperature, humidity, EC, pH, NPK — powering variable fertilization and smart irrigation
Weather Stations
12-parameter automated collection providing key inputs for pest models
Satellite Remote Sensing
Receives satellite data, AI-interpreted to generate NDVI, LAI, and other products
AgentSH in Agriculture — Multi-Agent Collaboration
AgentSH provides a complete agricultural agent orchestration and management infrastructure, addressing heterogeneous system integration, edge deployment, and multi-agent coordination challenges.
Agent Registry & Discovery
Supports any protocol (MQTT/CoAP/HTTP/gRPC) for agricultural devices and AI models.
Edge-Cloud Runtime
Supports seamless agent migration and checkpoint-resume between field edge nodes and the cloud.
Multi-Agent Workflow Engine
Supports both DAG and state-machine orchestration modes.
Built-in Security
TEE-based trusted data exchange and attribute-based access control.
Agent Communication Standard
Unified message routing, state synchronization, and capability negotiation.
Message Bus
AgentSH core — orchestrated 12 heterogeneous agents in Xinjiang cotton zone. Irrigation water saved 28%, pesticide use down 35%, cotton yield up 18%.
The AI-Driven Smart Agriculture Era Is Accelerating
From the National Smart Agriculture Action Plan's policy guidance to multi-agent LLM breakthroughs, from precision agriculture point solutions to full-process coverage, AI agents are driving an unprecedented paradigm shift in agriculture.
LLMs provide unprecedented agricultural knowledge understanding and reasoning, precision agriculture agents extend digital intelligence to every inch of soil, IoT sensor networks build the sensory nerve endings, and platforms like AgentSH integrate isolated AI capabilities into systematic, collaborative solutions.
With continued breakthroughs and deep integration, an AI-driven green, efficient, sustainable smart agriculture era is accelerating toward us.