FOREST WILDFIRE DETECTION IN A MACHINE VISION COURSE EXPERMINT USING VGG 19 AND DEEP LEARNING

Authors

  • Dr.S.JAGADEESH, PAMIDI SAI JAYASRI, KOMMIREDDI TRIPURA, JANGITI RUTHIKA

Keywords:

Forest wildfire detection, deep learning, VGG19, transfer learning, image classification, remote sensing, wildfire monitoring, convolutional neural networks, wildfire prediction, environmental monitoring

Abstract

Forest wildfires represent a significantenvironmental threat, necessitatingprompt detection for effectivemitigation. Traditional methods oftenfall short in providing timely alerts.This study explores the application ofdeep learning, specifically the VGG19architecture, for real-time

References

Habbib, A., & Khidhir, B. A. (2023). Forest Fire Detection Using Deep CNNs. Fire, MDPI.

Reis, L. M., & Turk, T. (2023). Transfer Learning Approaches for Forest Fire Detection. Remote Sensing.

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Published

2024-04-23

How to Cite

Dr.S.JAGADEESH, PAMIDI SAI JAYASRI, KOMMIREDDI TRIPURA, JANGITI RUTHIKA. (2024). FOREST WILDFIRE DETECTION IN A MACHINE VISION COURSE EXPERMINT USING VGG 19 AND DEEP LEARNING. Journal of Computational Analysis and Applications (JoCAAA), 33(4), 772–778. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2494

Issue

Vol. 33 No. 4 (2024): Selected Papers on Computational Techniques Across Multidisciplinary Fields: Innovations and Applications

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.