LoRa Temperature & Humidity Sensor
The 2-in-1 LoRa agricultural sensor offers reliable wireless monitoring of temperature and humidity, making it ideal for greenhouse and environmental applications. Featuring long range LoRa communication (up to 3KM line-of-sight), it delivers accurate data even in remote locations. With a wide detection range, durable wall-mounted design, and battery-powered operation, it ensures continuous, low-maintenance performance for optimized crop and climate management.
No HEAT Stress
| Type | Wireless temperature and humidity sensor |
|---|---|
| Place of Origin | Guangdong, China |
| Warranty | 1 year |
| Model Number | XZ-DSF1-TH1 |
| Material | ABS plastic |
| Product Name | LoRa Wireless Temperature Humidity Sensor |
| Description | Greenhouse Temperature and Humidity Sensor |
| Measuring Range | -40°C~+80°C, 0~100%RH |
| Power Supply | ER26500 3.6V Lithium battery |
| Battery Life | 5 years @ 8700mAH |
| Battery | Replaceable by user |
| Wireless Distance | Up to 5KM line of sight |
| Transmission Interval | 5 mins default, 1~240 mins settable |
| Working Frequency | 480 / 868 / 915 / 433 / 925 MHz |
| Protection Level | IP67 |
| Single Package Size | 15 × 10 × 5 cm |
| Single Gross Weight | 0.300 KG |
Features and Data
To Hot ? to cold? to Humid?
stop gessing and start knowing!
Specific advantages of temperature and humidity sensors
Reliable temperature and humidity sensors are standard equipment in modern greenhouses and controlled environment agriculture (CEA) facilities. They provide continuous data on the internal climate and make patterns visible 24/7 also while you are not on site.
Automatic Climate Control
The measured values can be used to directly control heating, ventilation, shading and humidification or dehumidification. This brings the climate back into the target zone. Thus reduces plant stress and often results in higher and more uniform yields over time.
More Information on: ResearchGate ,open-access papers published by MDPI.
Energy Optimisation
Every unnecessary temperature peak or excessive dehumidification costs energy. In practice, this leads to lower heating and cooling demand without compromising crop performance. Many research projects focus on optimising the balance between climate control and energy use.
More on ResearchGate.
Disease Prevention
High relative humidity (often above 80–90 %) together with condensation on leaves strongly favours fungal diseases such as Botrytis (grey mould) and various powdery or downy mildews. Well placed temperature and humidity sensors help to detect these critical humidity phases early on. They often occur during late evening or during the night when no person is on site.
Pacific Northwest Plant Disease Management Handbook
Penn State Extension.
What can be calculated from temperature and humidity?
Vapor Pressure Deficit (VPD)
In practice, VPD is the most important derived parameter from temperature and RH.
Definition: Difference between the saturation vapour pressure of the air at the current temperature and the actual vapour pressure at a given air humidity.
Meaning of VPD:
- Shows how ‘thirsty’ the air is: drives transpiration.
- VPD too low: air ‘full’ of water, low transpiration, high risk of disease.
- VPD too high: plants close stomata, stress, growth inhibitors
Practical benefits:
- Run target VPD instead of just target temperature
- Store target VPD ranges by crop + development stage
Dew point & condensation risk
The atmospheric temperature (varying according to pressure and humidity) below which water droplets begin to condense and dew can form.
When the surface temperature (leaf) falls below the dew point, condensation occurs which can lead to fungal deceases (botrytis, mildew, etc.).
Practical use:
Dew point alarm at night when RH is close to 100%.
Control strategies such as: briefly heat up + ventilate before the dew point is reached.
Disease risk indices
Many fungal diseases (botrytis, downy mildew, etc.) are highly dependent on temperature and RH/leaf wetness duration.
With temperature and humidity data, you can, for example:
Count hours where the Humidity is above 85–90% which serves as an indicator of leaf wetness and risk of infection.
Such models are used in the literature and can be integrated into decision support systems and AI models.
