Forest Fire Detection and Prediction using IoT and Node MCU

  • Truptali Anil Aundhkar Research Scholar, Department of Electronics And Communication Engineering Dr.D.Y.Patil Technical Campus Talasande


This paper presents a fire detection and alert system based on IoT. Here a specific environment is monitored 24×7 and the user is alerted in case of any fatal situation. This can be implemented using a nodemcu and a number of sensors for detecting different physical parameters that can go high during a fire-related accident. Nodemcu is an IOT-based controller board with an onboard WIFI module called ESP8266. Here two parameters are being monitored continuously temperature and the presence of smoke. For sensing temperature, the LM35 temperature sensor is used. For sensing the presence of smoke a gas sensor called MQ6 is used, this sensor can detect and measure any carbon-based gas smoke that produces CO2 which makes it possible to detect smoke using MQ6. Also, a PIR sensor is used for detecting the presence of any human. Both gas and temperature sensor is analogue so cannot be connected to a digital pin but to an analog pin that is input to an ADC (analogue to digital converter). Nodemcu has a one-channel ADC which presents a problem since there are two analog sensors. This can be solved by adding an ADC module called MCP3008. MCP3008 is a sixteen-pin IC with an eight-channel ADC. Also, forest areas can be monitored through a camera using this camera fire can be detected using image processing. For this we can run a Python program from a PC and using the OpenCV library fire can be detected. This can be used as a contingency system if the sensors malfunction. If any fire is detected then a water sprinkler will be turned on to prevent fire also an alert is sent to the concerned person for further action.


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How to Cite
ANIL AUNDHKAR, Truptali. Forest Fire Detection and Prediction using IoT and Node MCU. Journal of Advanced Research in Electronics Engineering and Technology, [S.l.], v. 10, n. 2, p. 11-15, jan. 2024. ISSN 2456-1428. Available at: <>. Date accessed: 21 june 2024.