AI, IoT, and decision support tools Vital to agricultural modernization

In the face of mounting climate threats and food security demands, Colombia’s agricultural sector finds itself at a pivotal moment. While global adoption of digital tools in farming accelerates, smallholder farmers in developing nations remain constrained by limited infrastructure, high costs, and policy fragmentation. A new systematic review highlights how Information and Communication Technologies (ICTs) could bridge this gap, if critical technical and administrative barriers are addressed.

The findings are detailed in the peer-reviewed study “Information and Communication Technologies Used in Precision Agriculture: A Systematic Review”, published in the June 2025 issue of AgriEngineering. Conducted by researchers from institutions across Colombia, Mexico, and Pakistan, the review examines technological needs, existing limitations, and implementation strategies tailored to Colombia’s context.

What are the core needs for precision agriculture in Colombia?

Colombia’s agricultural output, especially for staple crops like corn, is challenged by outdated practices such as land burning, over-irrigation, and chemical overuse. The study classifies farms into five levels of technological maturity. At the lowest level, farms lack even basic data for crop management. At the highest level, farms integrate mobile applications, decision-support dashboards, IoT sensors, and AI-driven predictive tools to optimize productivity and sustainability.

To reach higher levels of digital maturity, the study outlines a comprehensive roadmap:

• Traceability systems to ensure product transparency from seed to consumer, often via QR codes or blockchain.
• Environmental footprint metrics, including carbon, water, and ecological impact, to meet global sustainability standards.
• Decision support tools (DSS) powered by big data and AI, enabling farmers to make informed choices about irrigation, fertilization, and crop protection.
• Remote sensing and automated monitoring, including drones, satellite imagery, and ground sensors, for real-time agronomic analysis.
• SCADA systems for remote control of greenhouse and field systems, including irrigation and CO₂ levels.

These technologies feed into a broader digital infrastructure that the authors argue should become the backbone of a national precision agriculture framework.

Which technologies are already in use and what gaps remain?

The study benchmarks Colombia’s digital agriculture status against Brazil, Mexico, and Kenya. While Brazil leads with high adoption of GPS-guided tractors and drone systems supported by national ag-tech programs, Colombia lags behind with only pilot-level implementations of early warning systems (EWS) and low-cost IoT devices.

Specific technologies evaluated in the review include:

• Mobile-enabled traceability systems, such as smartpens and tablets used by field workers.
• Big Data analytics for unstructured sensor data from soil, weather, and crops.
• Blockchain-based traceability to secure supply chain data and build trust with consumers.
• Machine-integrated communication protocols, like ISOBUS and CAN, enabling seamless farm machinery management.

Yet significant gaps persist. Most Colombian farms lack digital literacy, real-time monitoring platforms, and robust connectivity—especially in rural zones like southern Atlántico. Moreover, many current systems are fragmented, uncalibrated, or dependent on low-cost sensors that cannot capture geospatial or microclimate data adequately.

How can precision agriculture advance despite socio-technical barriers?

The study calls for urgent multi-sectoral collaboration to overcome Colombia’s economic, infrastructural, and institutional hurdles. Key recommendations include:

1. Government-backed funding and subsidies for smallholder farmers to access IoT hardware, mobile apps, and training.
2. Improved connectivity infrastructure, especially in remote areas, to support real-time data flows from farms to national databases.
3. Integration of early warning systems (EWSs) as a foundational step toward climate-resilient agriculture. EWSs detect weather anomalies and trigger alerts through predictive models—enabling adaptive crop strategies and water resource management.
4. Development of DSS platforms tailored to Colombian crops, ecosystems, and farmer profiles, using machine learning to synthesize environmental, economic, and phenological indicators.
5. National data architecture for unified agricultural information, enabling interoperability between governmental databases, remote sensors, and local farmers’ dashboards.

The authors underscore that ICTs must not be imported indiscriminately but adapted to local socio-economic realities. Case studies from Kenya, Mexico, and Brazil offer models for integrating SMS-based alerts, mobile extension services, and rural innovation centers that have improved productivity in similarly constrained environments.

With empirical field validation and a policy focus on EWSs and AI-enabled decision support, Colombia could leapfrog into a more resilient and productive agricultural era. The study recommends prioritizing interoperable, scalable systems that combine climate prediction with autonomous actuation, effectively industrializing the rural sector.