<![CDATA[Peatland fires are a severe threat to the global environment. Existing peatland fire early detection systems commonly detect parameters such as air temperature, humidity, gas, smoke, and fire. This paper proposes a new peatland fire early detection method using the twig moisture content parameter. This method utilizes the most significant parameter approach for fire vulnerability compared to current peatland fire early detection systems. In particular, we developed an internet of things (IoT)-based twig dryness sensor to realize a field-applicable system. We propose a twig dryness sensor using the resistive sensing method, which employs a needle electrode to measure twig moisture content. Using the twig dryness sensor, three classifications of flammability were obtained, namely very difficult (moisture above 30%), difficult (moisture between 5%-30%) and easy (moisture less than 5%). This device utilizes readily available compact and portable materials. This instrumentation is digitally controlled with a low- power consumption microcontroller and long range (LoRa) transmitter, providing a long-life battery and long-range data transmission. Sensor data visualization is presented as twig dryness values and categorized according to fire vulnerability levels. The proposed system provides real-time and sustainable measurement.]]>
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