Overview
Grove-Temperature & Humidity Sensor Pro is a high accuracy temperature and humidity sensor based on the DHT22 module (also known as AM2302 or RHT03). High-cost performance and high precision make it ideal for temperature and humidity monitoring of Arduino and Raspberry Pi, you can also use it to make a thermometer and hygrometer.
The DHT22 includes a capacitive humidity sensor and a high precision temperature sensor. The range of humidity sensor is 0 to 99.9 %RH with ±2% accuracy while the temperature sensor ranges from -40 to 80℃ with ±0.5℃ accuracy. With the help of a built-in 8-bit microcontroller, the DHT22 converts the analog output of those two sensors to the digital signal, and output both temperature and humidity data via a single pin.
Compared with the DHT11, this product has higher precision and wider measurement range, but the usage and code are almost the same. Simply put, if you need higher measurement accuracy, this product will be a better choice.
Tech specs
Item |
Min |
Norm |
Max |
Unit |
VCC |
3.3 |
- |
6 |
V |
Measuring Current Supply |
1 |
- |
1.5 |
mA |
Standby Current Supply |
40 |
- |
50 |
uA |
Measuring range (Humidity) |
5% |
- |
99% |
RH |
Measuring range (Temperature) |
-40 |
- |
80 |
°C |
Accuracy(Humidity) |
- |
- |
±2% |
RH |
Accuracy (Temperature) |
- |
- |
±0.5 |
°C |
Resolution (Humidity) |
- |
- |
0.1% |
RH |
Resolution (Temperature) |
- |
- |
0.1 |
°C |
Repeatability(Humidity) |
- |
- |
±0.3% |
RH |
Repeatability (Temperature) |
- |
- |
±0.2 |
°C |
Long-term Stability |
- |
- |
±0.5% |
RH/year |
Signal Collecting Period |
- |
2 |
- |
S |
Respond Time 1/e(63%) |
6 |
- |
20 |
S |
Get Inspired
As Jallson Suryo discusses in his project, adding voice controls to our appliances typically involves an internet connection and a smart assistant device such as Amazon Alexa or Google Assistant. This means extra latency, security concerns, and increased expenses due to the additional hardware and bandwidth requirements. This is why he created a prototype based on an Arduino Nicla Voice that can provide power for up to four outlets using just a voice command. Suryo gathered a dataset by repeating the words “one," “two," “three," “four," “on," and “off” into his phone and then uploaded the recordings to an Edge Impulse project. From here, he split the files into individual words before rebalancing his dataset to ensure each label was equally represented. The classifier model was trained for keyword spotting and used Syntiant NDP120-optimal settings for voice to yield an accuracy of around 80%. Apart from the Nicla Voice, Suryo incorporated a Pro Micro board to handle switching the bank of relays on or off. When the Nicla Voice detects the relay number, such as “one” or “three," it then waits until the follow-up “on” or “off” keyword is detected. With both the number and state now known, it sends an I2C transmission to the accompanying Pro Micro which decodes the command and switches the correct relay. To see more about this voice-controlled power strip, be sure to check out Suryo’s Edge Impulse tutorial.