Building a “smart barrier” for food security

2025-07-31

Amid a global wave of digitalization, China’s food security efforts stand at a new turning point with the rise of smart agriculture. With its revolutionary capacity for data-driven insights, intelligent decision-making, and precision operations, smart agriculture has become a vital tool for safeguarding national food security. However, critical bottlenecks—such as fragmented data silos, underdeveloped foundational models, and lagging intelligent equipment—must be addressed. Only by advancing data integration, model innovation, and equipment upgrades in tandem can China build a solid “smart barrier” for food security and empower its grain reserves with the full strength of science and technology.

Breaking data silos

Agricultural production data in China is fragmented across regions and stakeholders, with varied formats and types, forming numerous “data islands.” A shared infrastructure for agricultural big data must be established to aggregate agricultural information from different areas. Dedicated funding should support integrated research on data collection methods, such as satellite remote sensing, aerial imaging, and ground-based sensors, balancing cost and accuracy. Additionally, general platforms for configuration optimization and performance simulation should be developed. A “centralized + distributed” integrated cloud architecture can help ingest decentralized data sources while addressing data security and resource consolidation challenges.

Drawing from Europe’s GAIA-X data space initiative, China should formulate unified data standards and require relevant departments and entities to share farmland, climate, and market data. Cross-sectoral data analysis should be supported to break down data silos within agriculture.

Accelerating foundational model development

China’s agricultural modeling efforts suffer from fragmentation, weak capacity, and slow progress. Innovative mechanisms are needed to stimulate breakthroughs. A “competitive selection + open testing” approach can be adopted: national research funds can sponsor open competitions for critical models—such as staple crop growth and livestock behavior—where multiple teams submit entries for public evaluation, with winning models receiving focused support. All outcomes should be open-source and publicly available.

High-level algorithm challenges with attractive prize incentives should be launched to engage academia, industry, and research institutions, encouraging model innovation and continuous iteration within open-source communities. A tightly integrated industry-academia-research consortium should also be built, promoting a model in which “industry proposes problems, government gives support, and the two collaboratively develop solutions.” Targeted research should focus on practical pain points, supported by mechanisms for sharing research findings and incentivizing commercialization.

Moreover, robust computational capacity and an open-source ecosystem are the foundation for model innovation. A dedicated cloud platform and computing subsidies should be provided via the national supercomputing network. Open-source communities and public testing platforms should be developed, with dedicated funds to support model maintenance. Low-code APIs must be created to drastically lower the threshold for model deployment and reuse, accelerating the growth of a thriving model ecosystem.

Upgrading intelligent agricultural machinery

Currently, China’s smart agricultural machinery remains heavily reliant on foreign technology. Core components such as high-end sensors, electronic control systems, and new-energy powertrains still rely heavily on imports, and key technologies remain vulnerable to chokepoint risks. Moreover, hardware and model algorithms are often disconnected, lacking the chip-level integration needed for real-time execution.

To address this, a collaborative platform should be built for joint R&D across industry, academia, and research sectors, with a focus on overcoming these chokepoints. Core breakthroughs are needed in high-precision agricultural sensors, new-energy power systems, electronically controlled hydraulic modules, corrosion-resistant materials, and digital manufacturing techniques. Model algorithms must be embedded into agricultural equipment, enabling intelligent field operations. Chip-level integration of agricultural model algorithms should be promoted in smart machines, robots, and drones, allowing them to autonomously adjust operational parameters in response to real-time data—enhancing efficiency and precision.

A certification system for “equipment + models” should be established to promote seamless integration. Companies should be incentivized to collaborate on embedding agricultural models into domestically produced smart machinery. Special support should be given to model-integrated development of agricultural robots and other intelligent equipment.

Xiong Hang, Professor at the College of Economics and Management, and Executive Dean of the Institute of Digital Agriculture, Huazhong Agricultural University