In the first step, sensor nodes are deployed across forests to create a connected network. These nodes continuously measure and provide real-time environmental data for accurate monitoring.

In the second step, the sensor network “reads" the forest like a living system. Data from the nodes is sent through LoRa to the central server, allowing efficient, long-range, and low-power transmission. 

In the third step, the system works on two layers. The first layer focuses on real-time detection, monitoring sensor data to identify anomalies and calculate the probability of a forest fire. The second layer acts as a forecasting engine, predicts wildfire risks weeks in advance.

In the final step, if a fire risk is detected, alerts are generated in real time. Beyond alerts, a user-friendly interface also allows users to view overall risk levels and monitor environmental parameters at any time.