The reason why LoRaWAN solar soil EC sensor can become the "soil doctor" of smart agriculture lies in its deep integration of soil conductivity (EC) precise sensing technology, solar autonomous power supply technology, and LoRaWAN low-power long-distance transmission technology, achieving the core requirements of "no wiring, long-term duty, and precise monitoring". Its working principle can be broken down into four key modules, forming a complete closed loop from soil parameter collection to data terminal application.

1、 Core Perception Layer: Measurement Principle of Soil EC Value and Associated Parameters

The core function of sensors is to accurately capture soil EC values (reflecting salinity/fertility), moisture, and temperature. The measurement principles of these three parameters directly determine the accuracy of the data and are also the basis for guiding agricultural management.

• Soil EC value (conductivity) measurement: quantitative capture of ion conductivity characteristics

The soil EC value is essentially an indicator of the conductivity of soluble ions (such as nitrogen, phosphorus, potassium, sodium, calcium, etc.) in the soil. The higher the ion concentration, the greater the EC value. The sensor adopts the dual electrode method (or four electrode method) to achieve EC value measurement, and the core principle is as follows:
Electrode structure: The sensor probe is equipped with 2-4 corrosion-resistant metal electrodes (usually made of 316 stainless steel or titanium alloy to prevent corrosion by soil salts). After insertion into the soil, the electrodes form a "conductive circuit" with the soil;
Signal excitation: The device applies a stable low-frequency AC voltage (usually 50-1000Hz to avoid soil polarization effects affecting measurement accuracy) to a pair of "excitation electrodes", forming a uniform electric field in the soil;
Current collection: Another pair of "measuring electrodes" synchronously collect the weak current generated by the directional movement of ions in the soil (the current size is positively correlated with the ion concentration);
Data calculation: Soil resistance is calculated based on Ohm's law (R=U/I), combined with geometric parameters such as electrode spacing and insertion depth. The soil conductivity is calculated using the formula EC=K/(R × L) (where K is the electrode constant and L is the electrode spacing), and the final output unit is μ S/cm or mS/cm.
Note: Compared with the dual electrode method, the four electrode method can effectively eliminate the interference of electrode soil contact resistance, and has higher accuracy in extreme scenarios such as saline alkali land. The measurement range can cover 0-20000 μ S/cm with an error of ≤ 3%.

• Soil moisture measurement: application of frequency domain reflectometry (FDR) technology

Soil moisture is closely related to EC value (moisture is the medium of ion transport), and sensors usually use FDR (frequency domain reflectometry) technology to measure soil volumetric moisture content. The principle is as follows:
High frequency signal transmission: The probe is equipped with a high-frequency oscillator, which emits high-frequency electromagnetic waves of 100MHz-1GHz to the soil. When the electromagnetic waves propagate in the soil, different "dielectric constants" will be generated due to different soil moisture contents (dry soil dielectric constant is about 3-5, pure water is about 80, and the higher the moisture content, the larger the dielectric constant);
Signal reflection and reception: Some electromagnetic waves are reflected back to the sensor by soil particles, and the receiving module captures the phase difference and amplitude attenuation of the reflected signal;
Moisture conversion: By using a preset "dielectric constant moisture content" calibration curve (which needs to be calibrated in advance for different soil types, such as clay, loam, and sandy soil), the characteristic values of the reflected signal are converted into soil volume moisture content (unit:%), with a measurement accuracy of ± 2% (0-50% moisture content range).

• Soil temperature measurement: temperature resistance characteristic conversion of thermistor

Temperature can affect the measurement accuracy of soil EC value and moisture (for example, an increase in temperature can accelerate ion movement, resulting in a larger EC value), so it is necessary to measure temperature synchronously for "compensation calibration". The core uses NTC thermistor:
Component characteristics: The resistance value of NTC thermistor decreases exponentially with increasing temperature, and it has the characteristics of high sensitivity (resistance change can reach thousands of ohms in the range of -40 ℃ to 80 ℃) and fast response (≤ 1 second);
Signal conversion: The device applies a constant current to the thermistor, measures the voltage change at both ends of the resistor (U=IR), infers the resistance value, and then compares it with the "temperature resistance comparison table" of the thermistor to convert the soil temperature, with an accuracy of ± 0.5 ℃ and a resolution of 0.1 ℃;
Compensation function: Real time temperature data is fed back to the EC value and moisture measurement module, and errors caused by temperature fluctuations are corrected through algorithms (for example, for every 1 ℃ increase in temperature, the EC value increases by about 2%, and the deviation needs to be deducted proportionally).

2、 Energy supply layer: complementary dual energy of solar energy and batteries

Sensors need to be unmanned in the field for a long time, so the solar powered autonomous power supply system is the guarantee for their stable operation, and the core is the collaborative work of "solar charging+battery energy storage":

• Solar energy conversion: efficient application of photoelectric effect

Solar panel selection: Single crystal silicon solar panels (with a photoelectric conversion efficiency of 20% -24%, higher than polycrystalline silicon) are used, with an area usually ranging from 50-100cm ². They can output 5-10 Wh of electricity under a daily average of 4 hours of light;
Charging management: equipped with MPPT (Maximum Power Point Tracking) charging controller, real-time tracking of the maximum power output point of the solar panel (such as automatically adjusting voltage and current when the light intensity changes to avoid energy waste), efficiently transmitting electrical energy to the battery;
Anti reverse charging protection: When there is no light at night or in rainy weather, the controller automatically cuts off the connection between the solar panel and the battery to prevent the battery from discharging in reverse to the solar panel and extend the battery life.

• Battery energy storage: Long term low self discharge design

Battery type: Using lithium thionyl chloride battery (Li SOCl ₂), the capacity is usually 4000-19000mAh, with ultra-low self discharge rate (annual self discharge ≤ 1%, far lower than the 5% -10% of lithium batteries), wide temperature working range (-55 ℃ to 85 ℃), and a lifespan of up to 6-10 years;
Energy allocation: The battery prioritizes supplying power to the "sensing module" (EC, moisture, temperature measurement) and "transmission module" (LoRa communication), only activating high-power components during measurement and transmission, and entering sleep mode (sleep current ≤ 10 μ A) when idle, maximizing battery life.

3、 Data transmission layer: Low power long-distance communication using LoRaWAN protocol

The EC value, moisture, and temperature data collected by sensors need to be remotely transmitted to a cloud platform, relying on the LoRaWAN protocol to achieve the communication requirements of "low power consumption, long distance, and wide coverage"

• LoRa physical layer: Spread spectrum technology for long-distance transmission

Modulation method: Using LoRa spread spectrum modulation technology (based on CSSChirp Spread Spectrum), the data signal is loaded onto a "linear frequency modulation signal" (such as linearly sweeping from 200kHz frequency to 400kHz). This method has strong anti-interference ability, and even if the signal is submerged by noise, it can still recover the data through demodulation;
Transmission distance: In open farmland scenes, the coverage radius of a single gateway can reach 5-15km; in obstructed scenes such as orchards and hills, the coverage radius is 2-5km, far superior to short-range communication technologies such as Bluetooth (100 meters) and Wi Fi (1 kilometer);
Power consumption control: Adopting the "Class A" working mode (a low-power category defined by the LoRaWAN protocol), the sensor only wakes up briefly during "upstream data transmission" (such as uploading data every 10-24 hours, with customizable intervals) and "downstream receiving instructions" (such as remotely modifying sampling intervals), and sleeps during the rest of the time, with a single transmission power consumption of only a few millijoules.

• Data transmission process: Link from sensors to the cloud

Local data processing: Sensors convert EC values, moisture, and temperature data into digital signals and compress and encode them (such as using JSON or binary formats to reduce data volume, with a single transmission of only 50-100 bytes);
Gateway reception and forwarding: Data is sent to nearby LoRaWAN gateways through LoRa RF modules. The gateway converts LoRa signals into Ethernet/4G signals and forwards them to cloud network servers (NS);
Cloud data parsing: The network server verifies the legitimacy of the data (such as device ID, encryption key), and then forwards it to the application server (AS). The application server parses the raw data into readable EC values (such as 800 μ S/cm), moisture content (such as 60%), temperature (such as 25 ℃), and stores them in the database.

4、 Data application layer: accuracy guarantee for calibration and compensation

The raw data needs to be calibrated and compensated before it can be truly used for agricultural decision-making, which is a key step for sensors from "data collection" to "value output":

• Soil type calibration: eliminate interference from soil texture

The particle structure and organic matter content of different soil types (such as clay, loam, sandy soil) vary, which can affect the measurement results of EC value and moisture. Sensors usually have built-in calibration libraries for multiple soil types (such as 10-20 common soils), and users can select matching soil types through mobile NFC or cloud platforms. The device automatically calls the corresponding calibration algorithm to correct measurement deviations (such as deducting the adsorption effect of soil particles on current when measuring the EC value of sand).

• Temperature and humidity cross compensation: correcting the impact of environmental factors

Temperature compensation: As mentioned earlier, for every 1 ℃ change in temperature, the EC value changes by about 2%, and moisture measurement may also have errors due to changes in dielectric constant. The equipment uses real-time collected soil temperature to linearly or nonlinearly correct the EC value and moisture data;
Air humidity compensation: The sensor host housing is equipped with an air humidity sensor. If the air humidity is too high (such as during the rainy season), it may cause condensation on the probe surface, affecting electrode conductivity. The device will determine whether to pause the measurement or correct the data based on the air humidity data.
Summary: Principle collaboration achieves "unmanned precise monitoring"
The principle of LoRaWAN solar soil EC sensor is essentially "multi technology collaboration": precise sensing of soil parameters is achieved through electrode method+FDR technology, outdoor power supply problems are solved through solar energy+lithium-ion batteries, long-distance low-power transmission is achieved through LoRaWAN protocol, and data reliability is guaranteed through calibration compensation algorithm. It is the seamless cooperation of these four modules that enables it to achieve the core value of "continuous output of high-quality soil data without manual intervention after deployment" in scenarios such as fields, orchards, and saline alkali land, providing a data foundation for precise management of smart agriculture.