A multimodal framework for Forest fire detection and monitoring

Forest fire is disastrous to civilizations due to damage to life and property. Forest fire results imbalance of the ecosystem loss of human life and wild animals. Early detection of fire is one of the ways to mitigate this problem. This article proposes a Multimodal framework to identify the fire-prone area of the forest. In this approach, the forest area is divided into different zones. In each zone, two types of sensors are deployed. One type of sensor senses the temperature, relative humidity, drought condition of that zone. Another one is the camera sensors that capture images of that zone simultaneously. All the sensors send the sensed data and image data to the base station. Base station predicts the status (High Active/ Medium Active/Low Active) of the forest zone applying the proposed Multimodal forest fire detection framework. This framework is the integration of the Neuro-fuzzy classification based Sensor model and CNN based Image model. From performance analysis, it is observed that the fire detection accuracy of this proposed Multimodal model is high compared to the individual Sensor and Image model. This model assists the base station in taking necessary action to mitigate fire at that zone in the forest.