Can augmented reality technology become the “unified operation interface” for smart farming?

As the global population continues to grow, climate change intensifies, and agricultural resources become increasingly scarce, traditional agricultural production models are facing unprecedented challenges. It is predicted that by 2050, global food production will need to increase by 70% to meet demand. Against this backdrop, “Agriculture 4.0” has gradually emerged, with embedded technologies such as the Internet of Things (IoT), artificial intelligence (AI), unmanned aerial vehicles (UAVs), and robotics widely applied in precision agriculture to improve production efficiency and sustainability.

However, these technologies often operate in silos, lacking a unified operation interface, resulting in data isolation and operational disconnects. Is there a technology that can integrate these scattered systems, allowing farmers to intuitively access information and make decisions directly in the field?

Professor Lauren Genith ISAZA DOMÍNGUEZ and colleagues from Universidad de Los Llanos in Colombia propose in a review that augmented reality (AR) technology may be a viable solution to this problem. The article systematically explores how AR can serve as a "central interface" to coordinate IoT, UAVs, agricultural robots, edge computing, and AI, constructing a responsive, data-driven smart agricultural system. The relevant paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025652).

AR technology can overlay digital information onto the real farmland environment, providing farmers with real-time visualized data through smart glasses or mobile devices. For example, when UAVs or ground sensors detect crop anomalies, the AR interface can guide farmers to the problem area. After scanning, edge AI performs image analysis, and upon confirming the issue, provides specific response recommendations—whether manual intervention or dispatching agricultural machinery for processing.

Currently, AR technology has been applied in multiple agricultural scenarios such as fertilizer and water management, pest and disease identification, crop harvesting, and agricultural machinery maintenance. For instance, an AR-integrated strawberry harvesting system can visually identify ripe fruits and mark them in real time, achieving a 93% success rate in selecting ripe strawberries; an AR-based irrigation system can combine sensor data with machine learning models to dynamically adjust irrigation schedules, reducing water waste.

In addition, the integration of AR with UAVs has provided new ideas for large-scale farm monitoring. Through the AR interface, farmers can directly view crop health status, soil variability, or pest and disease distribution on images captured by UAVs, and even conduct multi-user collaborative diagnostics to improve decision-making efficiency.

However, the article also points out that the current application of AR in agriculture still faces many challenges, including insufficient real-time data processing capabilities, limited adaptability of AI models in real-world environments, and the lack of unified communication standards between heterogeneous devices. Furthermore, the stability of gesture-based AR interaction in complex field environments, the technical framework for multi-UAV collaborative operations, and the development of low-cost AR solutions for resource-constrained regions are all key directions that need focused breakthroughs in the future.

To this end, the authors propose a series of future development directions, including building low-latency data pipelines, developing explainable AI interaction interfaces, advancing collaborative control between UAV swarms and AR, designing lightweight edge AI models, and enhancing data privacy protection through federated learning.

The article emphasizes that AR should not be merely regarded as an independent visualization tool, but rather as an integrated interface connecting on-site agricultural sensing, analysis, decision-making, and execution. With the continuous maturity of related technologies and the verification of integrated solutions, AR is expected to help farmers worldwide—especially resource-limited smallholder farmers—implement smart agriculture applications with lower thresholds and higher efficiency.