HIGH-PRECISION POSITIONING IN FARMING - Smart farming with 5G and real-time kinematics RTK

Farming in Germany is increasingly exposed to climate risks: Heat waves, droughts, heavy rainfall, storms, sudden cold periods and thus the consequences of climate change have long been a reality. These extreme weather events pose enormous challenges for farms. At the same time, the shortage of skilled workers is intensifying the situation. Artificial intelligence (AI) offers a promising solution to these complex problems. And the market is thus experiencing dynamic growth. According to forecasts, this sector is expected to expand at an average annual growth rate of 25.5% between 2024 and 2030. This is a clear indication of the increasing relevance of smart technologies in the agricultural sector. [1] These intelligent systems can be used in agriculture to promote sustainability, optimize cultivation strategies and improve animal welfare. It is not just about boosting efficiency and economic success but also about relieving farmers of physically demanding and time-consuming tasks.

1. Automation of machinery: Machine guidance with assistance systems

Farm machinery, which usually consists of tractors, seed drills and forage harvesters, offers enormous potential for the application of AI. Intelligent control systems enable these machines to work not only more precisely but also to have a more user-friendly design. When paired with drones or autonomous cleaning robots, routine activities can be automated to save valuable working time.

2. AI in animal husbandry: Monitoring feeding and behavior

AI is also opening up brand-new opportunities in livestock farming. Behavior analysis systems can detect abnormalities at an early stage and draw conclusions about the animals’ state of health. Simultaneously, AI can be used to enhance feeding – e.g., through demand-based quantity control and automated inventory management. This provides livestock farmers with more time for direct animal care while also improving husbandry conditions.

3. Nurturing and growing crops: Image data supports decision-making processes 

In the case of crop cultivation, AI assists with sorting seeds, detecting weeds and identifying unusable plants. With the aid of image recognition systems, these tasks can be automated and performed with a high level of precision. A key advantage in times of increasing water scarcity is that sensor-based AI solutions can also be used to control demand-based irrigation. [2]

5G and RTK technology for modern farming systems

The demands of modern farming systems in terms of precision, connectivity and availability can only be met with a modular IoT infrastructure. To achieve this objective, Telit Cinterion offers various modules designed for field use that can be scaled from individual sensor units to networked machinery (Fig. 1 to 4). These modules can be used to implement applications ranging from autonomous tractor navigation to networked field monitoring. 

• GNSS modules with multifrequency and RTK functionality enable centimeter-level position accuracy for automated tracks and precise field cultivation.
• 5G data cards ensure high transmission rates, e.g., for image transmission from drones, video streams or the connection of stationary systems.
• Compact LGA modules with integrated processing unit and OpenWRT support local data processing, e.g., in sensor stations or mobile routers.
• Cellular radio modules (5G, LTE, LPWA, NB-IoT) enable stable data connections with low latency, also for applications in remote regions.
• Edge and IoT gateways act as an interface between sensors, machine controls and cloud systems for the acquisition, transmission and evaluation of data.

All the components are designed for demanding fields of use (shock, vibration, temperature range from -40 °C to +85 °C) and can be scaled to a wide variety of devices and applications, from individual sensors to autonomous machinery. 

Table 1 provides an overview of how these components interact.

Table 1: Possible areas of application in smart farming and the applied modules and M.2 cards

Real-time data for the networked chicken farm

Modern farms increasingly rely on digital technologies to optimize their processes and use the available resources more efficiently. 5G wireless technology offers key advantages, especially where large volumes of data from various sources converge. A prime example is industrial poultry farming: In such plants, thermostats, automatic feeders and other sensors continuously record operating data. And although each individual sensor only generates small amounts of data, the sum total creates a complex network of information with thousands of data points. That said, connecting each sensor directly to its own 5G line would not be economically viable and would be way too complex from a technical point of view.

The solution is intelligent aggregation: When sensor data is aggregated in locally dimensioned clusters, the resulting data rate corresponds to the performance profile of a mobile 5G broadband connection. This allows the collected information to be efficiently transmitted to the central system via 5G, a process also known as “backhauling”. 5G is thus proving to be the ideal technology for bundling and transmitting data in networked agricultural systems. It enables not only high transmission speeds but also provides a flexible infrastructure that is able to keep up with the demands of modern farming. The 5G Release 17 standard expands the scope of applications: Satellite-based non-terrestrial networks (NTN) allow even remote barns without any cellular network coverage to be integrated into a digital barn monitoring system. Advances in cellular radio standards and chip designs also enable the use of new sensor classes that are both more energy-efficient and easier to integrate.

Real-time kinematics for precision farming

The accuracy of conventional GPS systems is often insufficient for automated farming applications. Centimeter-level position accuracy is required, particularly for the guidance of autonomous machines or the precise application of seeds, fertilizers and pesticides. This is achieved using real-time kinematic GPS (RTK-GPS), an extension of GNSS positioning using correction data.

RTK-GPS improves the repeatability and accuracy of mechanized work processes. Tracks can be documented precisely and reused in subsequent seasons. Seeds or fertilizers can also be placed with pinpoint accuracy. Table 2 displays the typical features of RTK. 

Table 2: Advantages of RTK technology in real-world applications

An RTK system consists of a stationary base station and a mobile rover unit (Fig. 5). The base station receives GNSS signals (e.g., GPS, Galileo, GLONASS) and compares them with its known position. Using the difference between the actual position and the satellite signal, it calculates correction data, which is transmitted to the rover in real time. This allows it to adjust its own position with centimeter-level accuracy.

In this application, the SE868K5-RTK module supports multifrequency operation (L1/L5) and various GNSS systems (Fig. 4). It is suitable for machine and drone navigation in RTK applications. Thanks to its compact form factor (11 × 11 mm), it can also be integrated into mobile devices with limited installation space.

Summary

IoT technologies have become an established cornerstone of modern, efficient and sustainable farming. Precise GNSS modules, powerful 5G connectivity and robust edge computing components enable smart, data-driven operations, including autonomous agricultural vehicles, connected animal husbandry and environmental monitoring.

The combination of RTK positioning and energy-efficient transmission technologies, such as NB-IoT and LTE-M, ensures reliable, resource-efficient processes, even in areas where conventional networks reach their limits. Expansion of satellite-based 5G networks (NTN) through Release 17 will help to eliminate dead spots and guarantee digitalization even at remote locations.

5G modules of the FN990B34/40 and FE990B series, as well as the GNSS module SE868K5-RTK, are designed for scalability and future proofing. They form the technological basis for automated processes, networked systems and intelligent logistics in tomorrow’s world of farming.

Sources:

[1] https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-in-agriculture-market#:~:text=The%20global%20artificial%20intelligence%20in%20agriculture%20market%20size,a%20CAGR%20of%2025.5%25%20from%202024%20to%202030

[2] pdf ki-news-impulspapier-landwirtschaft_web_final