• Naresh
• B.Himasri
• N.sowmya
• V.JoshithaRamani
• K.Prasanna

Forest fires pose a significant threat to ecosystems, biodiversity, and human settlements, wild life habitat. One of the primary causes of environmental damage is forest fires. To preserve forests from fires, early detection and preventive measures are required. To bring down these problems, we are implementing a model which uses Convolutional Neural Networks (CNN).With the use of convolutional neural network our model aims to verify whether a forest fire is noticeable in a picture. Our network is trained using a dataset that includes images divided into three categories: “fire”, ”no fire”, Images labelled “fire” have fire, and images labelled with “no fire” have no fire. Employing these techniques decrease false alarms and provides accurate fire detection results. The proposed system involves preprocessing of aerial images to enhance features relevant to fire detection. These images are then fed into CNN architecture trained to distinguish between normal forest scenes and those containing fire. Transfer learning techniques are employed to leverage pre-trained CNN models, optimizing performance even with limited training data. The trained model is capable of real- time detection and localization of fires with large forested areas.

Neural Networks, Convolutional, Fire Detection, Internet Of Things, CNN, Real time Detection, Localization

"FOREST FIRE DETECTION USING CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.9-16, October-2024, Available :http://www.jetir.org/papers/JETIRGN06002.pdf

"FOREST FIRE DETECTION USING CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp9-16, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06002.pdf