The agriculture industry is far smaller than the others in terms of employment, but it is strategically important, critical for European society’s well-being, and it accounts for the largest share of land use in Europe, amounting to 39.1% of total EU land in 2018, according to Eurostat. The EU’s agricultural industry created an estimated gross value added of EUR 222.3 billion in 2022. The distributed geographical nature and low density of workers in the agriculture industry are key reasons this industry needs to adopt CEI solutions.
This sector extensively uses animal tagging and increasingly uses field monitoring to optimise yields. Agricultural machinery is connected and smart, and it is moving toward autonomous operations. But much of the agricultural land has limited cellular network coverage. As a result, this industry is continuing to develop IoT use cases, using edge computing (such as systems onboard farm machinery) for automation and local analytics, as well as connecting to cloud resources for analysis across territories and asset fleets. The unique nature of the industry and strong adoption of CEI solutions make agriculture a strong candidate for evaluation in the context of CEI.
Spotlight use case categories
Asset Tracking and Monitoring
Agricultural organizations manage livestock, equipment, vehicles, facilities and other assets, which may be distributed across wide areas. Asset tracking enables these assets to reduce losses and thefts and to reduce the time and costs required for location and recovery. These solutions also enable improved veterinary care for animals and predictive maintenance for equipment, thereby reducing costs and increasing productivity. Asset tracking is a core use case in agriculture. According to the market analysis results, 50% of agriculture users already using some kind of solution for asset monitoring, maintenance and repair. other use-cases such as asset location monitoring, livestock monitoring, and animal tagging are also among the most adopted use-cases in this category. Due to the distributed nature of the assets, many such solutions incorporate tracking devices on livestock or equipment which connects over short-range wireless, cellular, LPWAN or satellite connection to central resources in the cloud or a data centre. In some cases, edge gateways may be utilized to collect and clean data before transmitting to the cloud. And because many agricultural fields and facilities are in rural areas, they may have limited connectivity, which may lead to some deployment of edge compute resources in or near those facilities.
- Asset tracking
- Fleet management
- Animal tagging, tracking and monitoring
- Equipment monitoring; predictive maintenance
- Animal health monitoring for veterinary care
Agricultural equipment manufacturers are adding sensors to farm machinery to enable increasing automation. For example, tractors can automate steering, speed, and raising and lowering of implements to ensure more accurate and reliable turns, aiding the operator and ensuring higher yield. Such automation utilizes sensors and AI for maximum precision in farm operations. Automation has the potential to deliver large efficiency gains to the industry. Equipment automation is a mostly nascent use case. Over time, it is expected to become more widespread within the offerings of the equipment suppliers. Equipment automation requires far more advanced hardware, software and analytics. Equipment automation requires extensive data processing and analytics on the machines themselves, resulting in significant usage of edge computing. However, these machines are also connected to central management systems and to the manufacturer's cloud for maintenance and other services.
- Automated tractors and farm equipment
- Turn automation
- Automated irrigation systems
- Milking and feeding systems
Precision agriculture combines data from various sources to optimise localised actions like irrigation and pest control. It increases yields, efficiency and decreases labour costs. It involves centralised cloud analysis, edge gateways, and heavy edge computing for specific components.
- IoT for visual inspection
- Drone-based inaging
- AI-based imagery analysis
- Machinery automation; precision spraying
- Agricultural robots; agricultural field monitoring
- Fleet management
Precision farming solution combining multiple types of ground micro-climate/soil/leaf information stations, agri-drones, semi-autonomous mobile robots and wearable devices.
Pilot done in organic olive plantations in Greece and NEMO will support technologies already implemented, e.g. Anti-frost system, irrigation system, etc.
Objectives and expected benefits
Reduce crop spraying and support organic olives harvesting while validating NEMO OS on heterogenous and fast-moving scenario.
Make intelligent decisions through real-time positioning and CF-DRL functions hosted on the IoT or at the edge.
Create extensive Data Sets to be shared as FAIR data via the European Open Science Cloud (EOSC)
Validate NEMO user acceptance from a farmer viewpoint evaluating bio-spraying efficiency, simplicity and cost