Smart Agriculture and Environmental Systems

The market analysts at Zion Market Research assume that the market for intelligent agricultural sensors will grow by 16 per cent a year, to around 59 billion US dollars in 2032. Four technology trends currently dominate: first, the miniaturisation and integration of MEMS sensors (micro-electro-mechanical systems), which offer compact, robust and cost-effective solutions for field and greenhouse applications. Second, the development of energy-self-sufficient sensor nodes through energy harvesting and ultra-low-power semiconductors, which allow maintenance-free continuous operation outdoors. Third, the on-chip integration of AI functions (edge AI), which enables local data pre-processing and real-time decisions directly at the sensor, for instance to detect drought stress or pest infestation. Fourth, the ongoing networking via low-power wide-area networks (LPWAN) such as LoRaWAN or NB-IoT, which support area-wide, cloud-connected sensing.

Their spread is increasing rapidly, driven by falling component costs, more capable embedded platforms and the growing pressure to raise yields while minimising environmental impact. The main systems in use are camera systems on tractors, drones and stationary measuring stations, often coupled with AI analysis in real time. Three technology trends currently dominate: first, the integration of AI directly on the sensor (edge AI); second, multispectral and SWIR extensions (short-wave infrared) for deeper insight into plant physiology; and third, ultra-low power consumption and miniaturisation for self-sufficient IoT networks.

Their spread is increasing worldwide, driven by advances in miniaturisation, falling component costs and the integration of AI functions at the edge. Three central technology trends currently shape the development: first, the integration of edge AI into energy-efficient MCUs, which makes local data analysis and pre-processing possible without a cloud connection, crucial for real-time monitoring in rural regions with unstable connectivity. Second, the ability to run machine-learning models directly on the device. This is enabled by ultra-low-power architectures (TinyML) running on MCUs. They are used, for example, to detect plant diseases through camera-based image analysis. Third, the convergence of multi-sensor fusion and adaptive communication: modern MPUs combine data from moisture sensors, spectral cameras and weather stations and adjust transmission protocols (LoRaWAN, NB-IoT, BLE) dynamically to energy availability and network quality, an approach that is already leading to significantly improved resource management in pilot projects.

The spread of such solutions is growing rapidly, driven by falling IoT semiconductor costs, better energy efficiency and the expansion of LPWAN and 5G networks into rural regions too. Current technology trends are shaping the next generation:

• Ultra-low-power MCUs with integrated radio (BLE 5/6, Zigbee, Thread) that run for years without a battery change.
• LPWAN standards such as LoRaWAN, NB-IoT and MIOTY for long-range, robust connections at minimal energy consumption.
• Multi-protocol SoCs that support several wireless standards in parallel and so increase scalability and future-proofing.

These trends make wireless systems smaller, cheaper and more reliable, and thus ready for mass use in precision farming, greenhouse automation or environmental monitoring.

While classic GPS systems still dominate today, modern semiconductor solutions increasingly rely on multi-GNSS chips that process signals from GPS, Galileo, GLONASS and BeiDou in parallel, a development that noticeably improves availability and accuracy even under difficult conditions such as forest or dense vegetation. Three central technology trends currently shape the field: first, the integration of RTK (real-time kinematic) into compact GNSS modules, which enables centimetre-accurate positioning for autonomous tractors, drones and robotic systems. Second, the combination of GNSS with 5G/NTN communication technologies, which allows robust, low-latency data transmission and cloud connection of distributed sensor nodes even in rural dead spots. Third, the miniaturisation and energy efficiency of semiconductor chips, which makes GNSS receivers integrable into wearables and distributed environmental measuring stations. In animal tracking, highly efficient GNSS semiconductor modules with an integrated low-power mode and support for various GNSS systems enable long-lasting, precise location of grazing livestock, wild animals and farm animals, even in remote or heavily wooded areas. By coupling this with IoT platforms and AI-supported movement analysis, health status, behavioural patterns and stress indicators can be identified early, which significantly improves animal welfare, herd management and the prevention of human-wildlife conflicts.

Particularly in hard-to-reach or expansive agricultural and environmental-monitoring applications, it allows operation without battery changes and so supports sustainable, scalable solutions. The main sources in use are photovoltaic systems, thermoelectric generators and vibration- and ambient-based energy sources such as RF- or magnetic-field-based systems. Three central technology trends currently shape the development: first, the miniaturisation and greater efficiency of semiconductor-based energy-harvesting chips, which work reliably even with weak ambient energy. Second, the combination of several energy sources in hybrid systems, such as photovoltaics plus thermoelectrics, to increase the overall energy yield and close supply gaps. Third, the integration of AI and IoT for dynamic, real-time optimisation of energy management, for instance through predictive load control or adaptive energy distribution.

With the boom in precise, data-driven agriculture and area-wide environmental measuring networks, the spread of these systems is rising rapidly; market analyses forecast double-digit growth rates for BMS worldwide by 2030. The main systems in use are IoT-enabled BMS for lithium-ion and LFP batteries in solar irrigation, agricultural drones, electric tractors and in autonomous weather and soil stations. New technologies are driving the development: AI-supported state estimation (SOC/SOH), improved thermal management, ultra-low-power semiconductors for energy harvesting and standardised communication protocols for cloud connection. Four trends dominate:

• integration of AI/ML for predictive maintenance and greater efficiency,
• solid-state and LFP batteries with more robust BMS algorithms,
• energy-harvesting solutions with highly efficient power management for self-sufficient sensor nodes,
• connected BMS architectures with 5G/LoRaWAN for real-time energy management in the agricultural smart grid.

In system development, architectural trade-offs have to be considered: smaller form factors and lower power consumption often conflict with high signal quality and durability, a balancing act that is particularly critical in the harsh agricultural environment. Power supplies must be designed to be especially robust, reliable and safe for use in extreme environmental conditions. Key requirements are a wide-range input voltage and high load stability, resistance to temperature and climate, a high degree of protection and mechanical durability, and reliable EMC and overvoltage protection.

Four technology trends dominate: first, wide-bandgap semiconductors such as SiC and GaN for highly efficient, compact power supplies in solar pumps or drones. Second, the integration of analog and digital functions into mixed-signal ASICs and power-management ICs for more robust, more intelligent systems. Third, reliable isolation technologies that guarantee high safety standards in harsh environments. Fourth, ultra-low-power components for battery-powered field sensors with long run times. Amplifiers, data converters and switching regulators remain key building blocks here, and their progress is a major force behind the digitalisation of agriculture and environmental systems.