Designing Smart Agriculture Systems: 5 Key Questions for Engineers
With around 570 million farms worldwide and accounting for roughly 17% of global GDP, agriculture plays a critical role in feeding a growing global population. At the same time, food demand continues to rise while the amount of available farmland is already close to its practical limits. As a result, improving productivity increasingly depends on technology.
Smart agriculture deployments combine sensors, connectivity, positioning and embedded intelligence to optimise farming operations and resource use. In a recent Avnet Silica webinar, experts from NXP, Nordic Semiconductor, Renesas, u-blox, Quectel, STMicroelectronics and Tria explored the technologies behind this transformation, from wireless IoT networks and high-precision GNSS to drones, edge AI and asset tracking platforms. Drawing on insights from the webinar, here are five key questions engineers should consider when designing smart agriculture systems.
1) What makes smart agriculture one of the most demanding IoT applications?
Smart agriculture is becoming one of the fastest-growing application areas for connected technologies. Currently, an estimated two billion sensors are deployed on farms worldwide. This number is expected to grow to around 20 billion by 2035 as connected agricultural systems continue to expand.
However, agricultural environments introduce system design challenges that differ significantly from many other IoT applications. Farms often cover large, geographically dispersed areas, frequently located in rural regions where communication infrastructure can be limited or inconsistent.
To operate reliably in these environments, smart agriculture systems typically rely on distributed sensing networks, long-range wireless connectivity and positioning technologies that enable accurate tracking of machinery, assets and livestock. Sensors monitor soil conditions, crop health and environmental factors, while connected machinery, drones and irrigation systems generate additional operational data across the farm.
Increasingly, these systems also incorporate edge processing capabilities, allowing devices and machines to analyse sensor data locally and respond in real time without relying entirely on cloud connectivity.
2) How can sensors and machines be connected across farms that may span kilometres?
Connecting sensors, machinery and monitoring systems across large farming areas presents a major networking challenge. Devices may be distributed over kilometres, often in locations where communication infrastructure is limited.
Engineers need to evaluate wireless technologies across several key factors, including power consumption, coverage range, scalability and overall system cost. These parameters determine whether a network can support large numbers of distributed devices while remaining economically viable.
Selecting the appropriate wireless technology is therefore a key architectural decision. Low-power technologies such as Bluetooth Low Energy (BLE) are well suited for short-range sensor communication, while cellular IoT technologies such as LTE-M or NB-IoT enable devices to connect reliably across larger distances.
New connectivity options are also emerging. DECT NR+ offers a scalable private network solution for industrial IoT deployments, while non-terrestrial networks (NTN) allow devices to connect via satellites, extending coverage into remote rural areas.
Today’s wireless IoT technologies already enable a wide range of smart agriculture applications, including environmental monitoring, smart irrigation, equipment management, livestock tracking, and connectivity for autonomous agricultural machinery.
3) Why is centimetre-level positioning becoming essential for precision agriculture?
Positioning technologies are a key enabler of precision agriculture systems. Modern agricultural machinery increasingly relies on accurate location data to support machine guidance and automated field operations.
Autonomous tractor guidance systems, for example, require highly accurate positioning to support field operations such as tilling, harvesting and unloading. In these systems, GNSS receivers combined with correction services can achieve positioning accuracy of less than 5 cm, enabling precise machine awareness and smooth vehicle control.
High-precision GNSS is therefore essential for precision farming. It allows machinery to follow straight driving lines, maintain uniform spacing between passes, and precisely follow field boundaries, helping reduce overlap and optimise resource use.
To achieve this level of precision, GNSS solutions typically combine satellite positioning with correction data from RTK (Real-Time Kinematic) services or reference stations, improving positioning performance beyond the metre-level accuracy of standalone GNSS receivers.
Smart Agriculture Webinar
Practical Innovations in Connectivity, Positioning & Edge Intelligence
This webinar brings together experts from NXP, Nordic Semiconductor, Quectel, Renesas, STMicroelectronics, Tria and u-blox to explore practical, deployable solutions that are shaping the next generation of modern agriculture.
4) What system architecture is needed for agricultural drone platforms?
Drone platforms are increasingly used in agriculture to survey fields and collect detailed data about crop conditions and environmental factors. Agricultural UAV (Unmanned Aerial Vehicle) systems integrate multiple electronic subsystems that support flight control, sensing and data processing. A central element is the vehicle management unit (VMU), which coordinates flight stabilisation and trajectory control. The VMU processes data from onboard sensors and communicates with motor control units (ESCs) that regulate the speed and thrust of the propulsion system.
To capture agricultural data, drones use sensing systems, such as cameras and environmental sensors. These sensors generate large volumes of data that can be processed by a companion computing platform, which handles tasks such as image processing and higher-level control functions.
Connectivity also plays an important role in UAV architectures. Communication modules support telemetry, remote operation and data transfer between the drone and ground systems. Power management and battery systems supply energy to propulsion, sensing and processing components, enabling reliable operation during aerial monitoring missions.
5) How is edge intelligence transforming smart agriculture systems?
As smart agricultural systems evolve, so does the way in which operators interact with them. Advances in embedded AI enable devices to operate more autonomously in the field. This is driving new approaches to human–machine interaction, moving beyond traditional control panels towards more intuitive interfaces.
Voice-based interfaces and speech-to-speech technologies are one example of this shift. By allowing operators to interact with equipment or management platforms using natural language, these systems can simplify access to system data, alerts and configuration settings in the field.
For engineers, this means designing systems that combine sensing, connectivity and processing with intuitive interaction technologies that simplify operation.
Nordic Semiconductor Smart Agriculture Solutions
Nordic Semiconductor’s ultra-low-power wireless and cellular IoT platforms form the foundation of flexible, high-performance connectivity architectures for modern Smart Agriculture systems.
Renesas Smart Agriculture Solutions
Renesas supports multiple wireless localisation approaches, allowing developers to select the most appropriate solution based on range, power consumption, and system architecture requirements.
NXP Smart Agriculture Solutions
At the core of NXP’s Smart Agriculture offering is the i.MX95 applications processor, positioned as a highly integrated platform for mixed-criticality robotics and industrial systems.
Quectel Smart Agriculture Solutions
Quectel provides high-performance GNSS and RTK solutions designed to support precision farming at scale, combining multi-band reception, correction service compatibility and integration-ready modules within a unified high-precision architecture.
STMicroelectronics Smart Agriculture Solutions
STMicroelectronics supports smart agriculture applications through its Teseo family of GNSS receivers and modules, which provide high-accuracy positioning capabilities for industrial and automotive applications.
Tria Smart Agriculture Solutions
By combining embedded computing hardware, optimised software pipelines, and AI acceleration technologies, Tria enables developers to implement intelligent agricultural systems capable of perception, reasoning, and natural interaction.
u-blox Smart Agriculture Solutions
By combining advanced receivers, correction services and high‑performance antennas, u‑blox enables OEMs and developers to build reliable positioning systems that deliver centimetre‑level accuracy in challenging agricultural environments.