5.1 Core Product Portfolio Overview

A complete smart agriculture environmental monitoring system is built from six categories of core hardware products. Each category serves a specific function in the sensing-to-cloud data chain. The image below shows the six core product types in their physical form, providing a visual reference for procurement and field identification.

Smart Agriculture Monitoring System Core Products

Figure 5.1: Core Product Portfolio — Six Hardware Categories for Smart Agriculture Monitoring Systems

The six core product categories are: (1) Automatic Weather Station (AWS), (2) LoRa Field Node, (3) Edge Gateway / RTU, (4) Multi-parameter Soil Sensor, (5) CO₂ and Air Quality Sensor, and (6) Water Quality Multiparameter Probe. Each product must be selected to match the specific requirements of the deployment scenario, as detailed in the subsections below.

5.2 Interface Connection Logic

Understanding the interface connection logic of the edge gateway is essential for correct system integration. The gateway serves as the central hub connecting field sensors (via RS-485 and analog inputs), actuators (via relay outputs), and the cloud platform (via 4G/Ethernet). The diagram below illustrates the complete interface logic of a typical agricultural edge gateway.

Edge Gateway Interface Connection Logic Diagram

Figure 5.2: Edge Gateway Interface Logic — Sensor Inputs, Actuator Outputs, and Cloud Connectivity

5.2.1 Interface Specifications Summary

The table below provides a complete specification of all gateway interfaces, including electrical characteristics, protocol support, and maximum load ratings. These specifications must be verified against sensor and actuator requirements before finalizing the system design.

InterfaceTypeQuantityElectrical SpecProtocolMax Load / Range
RS-485 PortsSensor input4 (isolated)±15V common modeModbus RTU32 devices per port
RS-232 PortSerial config1±12V, 3-wireAT commands / config1 device
Ethernet RJ45Network110/100 MbpsMQTT / HTTP / Modbus TCPN/A
4G LTE SIMCellular1 (Nano SIM)LTE Cat-1 / Cat-4MQTT / HTTPN/A
LoRa WANWireless1 (internal)868/915 MHz, 20 dBmLoRaWAN Class A/C1,000 nodes
Digital InputPulse / status8 channels5–30V DC, NPN/PNPPulse count / state10 kHz max pulse
Relay OutputActuator control4 channels250V AC / 30V DCOn/Off / PWM10A per relay
Analog Input4–20 mA sensors4 channels0–20 mA, 12-bit ADCDirect measurement±0.1% full scale
Power InputDC power19–36V DC wide rangeN/A15W max consumption
USB ConfigConfiguration1 (Micro-USB)USB 2.0Virtual COM portConfig only

5.3 Core Product Feature Comparison Table

The following table presents a comprehensive feature comparison of the six core product categories, covering measurement parameters, accuracy, communication interfaces, power requirements, environmental ratings, and key selection criteria. This table serves as the primary reference for product selection decisions.

Feature Weather Station AWS-Pro LoRa Field Node LFN-200 Edge Gateway EG-RTU-X1 Soil Sensor SS-5Pro CO₂ Sensor CO2-800 Water Quality WQP-6
Primary Parameters T, RH, Wind Speed, Wind Dir, Rain, Solar Rad, Pressure T, RH, Soil VWC, Soil T (configurable sensors) Data aggregation, protocol conversion, control VWC, Soil T, EC, pH, N/P/K (5-in-1) CO₂ (NDIR), T, RH, TVOC, PM2.5 (optional) DO, pH, Turbidity, Water T, EC, ORP
Accuracy (Key Param) T: ±0.3°C; RH: ±2%; Wind: ±0.3 m/s T: ±0.5°C; VWC: ±3% (calibrated) N/A (gateway device) VWC: ±2%; EC: ±2%; T: ±0.5°C CO₂: ±50 ppm + 3%; T: ±0.3°C DO: ±0.1 mg/L; pH: ±0.05; T: ±0.1°C
Communication RS-485 Modbus RTU LoRa 868/915 MHz + RS-485 RS-485 ×4, 4G LTE, Ethernet, LoRa RS-485 Modbus RTU RS-485 Modbus RTU RS-485 Modbus RTU
Power Supply 12–24V DC (2W typical) Solar 5W + LiPo 10,000 mAh 9–36V DC (15W max) 12–24V DC (0.5W typical) 12–24V DC (1.5W typical) 12–24V DC (2W typical)
IP Rating IP65 (sensors), IP54 (junction box) IP67 (fully sealed) IP65 (metal enclosure) IP68 (buried installation) IP65 (indoor/outdoor) IP68 (submersible to 10m)
Operating Temp. -40°C to +60°C -20°C to +55°C -20°C to +60°C -20°C to +60°C -10°C to +50°C 0°C to +50°C
Calibration Interval 12 months (wind sensors) 12 months (soil sensors) N/A 12–24 months 12 months (NDIR auto-cal) 1–4 weeks (biofouling)
Modbus RTU Yes Yes Yes (master) Yes Yes Yes
Solar Power Option Optional Built-in Optional No No No
Data Logger Optional (SD) Yes (internal) Yes (SD card) No No No
Typical Price Range $800–$3,000 $150–$400 $300–$800 $80–$250 $120–$350 $400–$1,200
Primary Application Reference weather, ET₀ calc Distributed field monitoring Central data hub, control Precision irrigation, soil health Greenhouse CO₂ control Aquaculture, hydroponics

5.4 Sensor Selection Decision Matrix

The selection of specific sensors for each deployment must be driven by the crop's critical environmental parameters, the required measurement accuracy, and the available budget. The following decision matrix maps crop types to their priority sensor requirements, providing a structured starting point for sensor selection.

Crop / ApplicationPriority 1 SensorPriority 2 SensorPriority 3 SensorOptional Sensors
Greenhouse vegetablesCO₂ sensorT/RH sensor (canopy)Soil VWC/EC probePPFD sensor, leaf wetness
Open-field grain cropsSoil VWC (multi-depth)Weather stationRain gaugeLeaf wetness, wind sensor
Orchard / fruit treesFrost sensor (canopy T)Leaf wetness sensorSoil VWC (root zone)Wind speed, solar radiation
Aquaculture pondsDissolved oxygen probepH sensorWater temperatureTurbidity, NH₃-N, water level
Vertical farm / hydroCO₂ sensorPPFD sensorEC/pH (nutrient solution)T/RH per tier, DO (hydro)
Pasture / livestockWeather station (full)Solar radiationWind speed/directionSoil moisture, water level
Rice paddyWater level sensorWater temperatureWeather stationSoil temperature, rain gauge
Tea gardenT/RH (canopy)Leaf wetness sensorSoil moistureFog sensor, solar radiation

Selection Tip: Always start with the minimum viable sensor set (Priority 1 + 2) and expand based on operational experience. Over-instrumentation in the first year leads to data overload and poor alarm management. Add sensors incrementally as the team develops the capacity to act on the additional data.


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