How IoT works in Polyhouse farming

IoT based Smart Farming

IoT–enabled precision farming is built on a stack of sensor, communication, compute, and data layers that continuously monitor soil, crop, and environmental parameters. Soil-moisture probes, temperature/humidity sensors, and VPD (Vapor Pressure Deficit) gauges feed data into microcontrollers (e.g., Raspberry Pi, Arduino) which relay readings via LoRaWAN or cellular networks to cloud platforms. There, specialized software ingests, stores, and analyses data—often with ML models—delivering actionable insights (e.g., irrigation timing, nutrient needs) through dashboards or SMS/WhatsApp alerts. By precisely matching inputs to plant demands, these systems can cut water use by 20–30 % and boost yields by 10–15 %, turning raw data into measurable gains in farm productivity.

1. Sensor Layer: Capturing Field Conditions

1.1 Soil-Moisture Sensors

Capacitive or dielectric probes measure volumetric water content in the root zone with ±2 % accuracy, enabling data-driven irrigation rather than guesswork

1.2 Temperature & Humidity Sensors

Devices like the Sensirion SHT 35 capture air temperature (±0.1 °C) and relative humidity (±1.5 %), from which systems compute Vapor Pressure Deficit (VPD)—a key metric for plant stress and transpiration management.

1.3 Specialty Probes

Advanced platforms integrate nutrient sensors (N, P, K detection) or spectroscopic leaf sensors for on-the-spot fertilizer recommendations, as pioneered by startups like Teralytic.

2. Edge Compute Layer: Local Processing & Control

2.1 Microcontrollers & Single-Board Computers

• Arduino boards handle simple analog-to-digital conversions and control relays for drip lines or valves.

• Raspberry Pi Zero W provides Linux-based compute for running local analytics, logging data, and managing connectivity modules like LoRaWAN or cellular modems.

2.2 Power & Enclosures

Solar panels with battery backups ensure 24/7 uptime in off-grid fields, while rugged IP 65-rated enclosures protect electronics from dust, rain, and insect ingress .

3. Connectivity Layer: From Field to Cloud

3.1 LPWAN Protocols

LoRaWAN and NB-IoT deliver low-power, long-range links (up to 10 km) at minimal cost—ideal for dispersed farms lacking cellular coverage.

3.2 Cellular & Satellite

Where higher bandwidth is needed (e.g., camera feeds, firmware updates), 4G/5G and even satellite IoT options backhaul data to centralized servers.

4. Cloud & Analytics Layer: Turning Data into Decisions

4.1 Data Ingestion & Storage

Platforms like AWS IoT Core, Azure IoT Hub, or open-source ThingsBoard handle millions of sensor messages per day, storing time-series data in TSDBs (e.g., InfluxDB) for fast retrieval.

4.2 Processing & Machine Learning

• Rule-based Engines generate threshold alerts (e.g., “soil moisture < 15 % → irrigate now”).

• ML Models (ARIMAX-LSTM, random forest) forecast yield, pest outbreaks, or price movements by correlating sensor data with historical records.

4.3 Visualization & Alerts

Dashboards (Grafana, Power BI) display field maps, trend charts, and KPIs. Farmers receive SMS, WhatsApp, or mobile-app notifications with precise recommendations—such as exact valve opening durations—ensuring actions are timely and data-driven.

5. Integration & Actuation: Closing the Loop

Sensors and analytics are linked to automated actuators—electrically controlled pumps, solenoid valves, or drone-spray systems—so that optimized schedules translate directly into water, fertilizer, or pesticide applications without manual intervention. This closed-loop control maximises resource use efficiency and yield outcomes.

6. Why It’s Not Just Convenience

1. Resource Efficiency:

   • Water usage cut by 20–30 %, fertilizer by 10–20 % through precise application.

2. Yield Gains:

   • Trials report 10–15 % higher yields from stress-mitigating irrigation and nutrient management .

3. Risk Reduction:

   • Early drought or disease detection minimises crop loss, safeguarding farm income.

4. Scalability:

   • Modular hardware and pay-as-you-grow subscription models make advanced precision farming viable for smallholders and large estates alike.

Together, these components form a holistic precision-ag system that transforms raw sensor streams into quantified productivity gains—demonstrating that IoT in farming is ultimately about efficiency and yield, not mere modern gadgetry.

References

1. WebbyLab, “Precision Farming Using IoT,” accessed May 2025. (webbylab)

2. Business Insider, “Farmers are using IoT to take the guesswork out of growing,” May 2025. (Business Insider)

3. Actility, “IoT in Precision Agriculture,” accessed May 2025. (Actility)

4. Agritech Digest, “IoT: The Future of Precision Farming,” 2024. (Agritech Digest)

5. IoT For All, “What is Smart Farming?” accessed May 2025. (IoT For All)

6. MIT D-Lab, “Internet of Things: Low Cost Sensors for Agriculture,” accessed May 2025. (MIT D-Lab)

7. MDPI, “Applying IoT Sensors and Big Data to Improve Precision Crop Production,” 2023. (MDPI)

8. Eastern Peak, “IoT in Agriculture: Use Cases,” 2023. (Eastern Peak)

9. AgroERP, “Implementing Precision Agriculture Software,” 2023. (AgriERP)

10. Wikipedia, “Precision Agriculture,” May 2025. (Wikipedia)

11. Scopus, “Precision agriculture: GNSS IoT Solution,” 2025. (arXiv)