Open Access Journal

ISSN : 2395-2717 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Electrical and Electronic Engineering(IJEREEE)

Monthly Journal for Electrical and Electronic Engineering

ISSN : 2395-2717 (Online)

Call For Paper : Vol 11, Issue 08, August 2024

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Internet of Things and AI Computer-vision Robot for Forest Surveillance and Monitoring

Author : Rohith B N 1 Bhagirathi N M 2

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Date of Publication :31st July 2021

Abstract: Main goal of this paper is to provide surveillance and monitor the forest conditions. For this we have used both Internet of Things and AI Computer-vision technologies. Sensors have been placed on robot which is movable in all direction and OV2640 camera along with esp32 cam has been placed to get video feed. Further this video feed is proceed to detect intruders using python IDE and OpenCV. Sensor data is sent to IoT server, which can be monitored. Main purpose of monitoring sensor data is to detect fire. Robot is controlled using RF transceivers, which has very good range and has stepper motor for accurate movement. Since RF transceivers are used we can also send sensor data to the user for emergency situation.

Reference :

    1. M. PETKOVIĆ, I. GARVANOV, D. KNEŽEVIĆ and S. ALEKSIĆ, "Optimization of Geographic Information Systems for Forest Fire Risk Assessment," 2020 21st International Symposium on Electrical Apparatus & Technologies (SIELA), 2020, pp. 1-4, doi: 10.1109/SIELA49118.2020.9167162.
    2. A. L. Latifah, A. Shabrina, I. N. Wahyuni and R. Sadikin, "Evaluation of Random Forest model for forest fire prediction based on climatology over Borneo," 2019 International Conference on Computer, Control, Informatics and its Applications (IC3INA), 2019, pp. 4-8, doi: 10.1109/IC3INA48034.2019.8949588.
    3. J. Collumeau, H. Laurent, A. Hafiane and K. Chetehouna, "Fire scene segmentations for forest fire characterization: A comparative study," 2011 18th IEEE International Conference on Image Processing, 2011, pp. 2973-2976, doi: 10.1109/ICIP.2011.6116285.
    4. G. Yang and X. Di, "Adaptation of Canadian Forest Fire Weather Index system and it's application," 2011 IEEE International Conference on Computer Science and Automation Engineering, 2011, pp. 55-58, doi: 10.1109/CSAE.2011.5952422.
    5. K. Muhammad, S. Khan, M. Elhoseny, S. Hassan Ahmed and S. Wook Baik, "Efficient Fire Detection for Uncertain Surveillance Environment," in IEEE Transactions on Industrial Informatics, vol. 15, no. 5, pp. 3113-3122, May 2019, doi: 10.1109/TII.2019.2897594.

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