A DEEP LEARNING APPROACH FOR FOREST FIRE DETECTION

Srikanth.Cheemaladinne; Mr. Yerrabathana Guravaiah

A DEEP LEARNING APPROACH FOR FOREST FIRE DETECTION

Authors: Srikanth.Cheemaladinne, Mr. Yerrabathana Guravaiah

Unique Paper ID: 177600
Volume: 11
Issue: 12
PageNo: 1005-1009

Keywords: Forest fire detection Convolutional Neural Network (CNN) Image classification Environmental monitoring.

Abstract:
Forest fires have devastating environmental, economic, and social impacts. Traditional fire detection systems often rely on manual reporting or satellite data with delayed response times. This research proposes a real-time, automated forest fire detection system using deep learning techniques. The system processes visual and sensor data collected from drones, surveillance cameras, and IoT devices, using Convolutional Neural Networks (CNNs) to detect fire and smoke patterns accurately. Additionally, it integrates environmental parameters such as temperature and humidity to improve prediction reliability. The proposed model achieves high accuracy and low latency, enabling early intervention and reducing forest loss. The system is scalable and adaptable for various forest environments. Forest fires are significant contributors to environmental degradation, causing adverse impacts such as loss of wildlife habitat, extinction of plant and animal species, and depletion of biodiversity and forest resources. To mitigate these effects, we propose a model leveraging Convolutional Neural Networks (CNNs) for forest fire detection. CNNs are effective for image classification tasks, capable of identifying key features within images to detect specific objects. Our approach aims to identify the presence of a forest fire in an image by training a CNN on a dataset labelled as "fire" and "no fire," where "fire" images depict visible flames and "no fire" images show fire-free scenes. We utilize Keras and TensorFlow libraries, which provide high-level APIs, simplifying the design and training of our model for accurate and efficient fire detection.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 1005-1009

A DEEP LEARNING APPROACH FOR FOREST FIRE DETECTION

Available:https://ijirt.org/Article?manuscript=177600

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