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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66113
Volume:
10 |
7 |
Jul 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Computer Science Engineering
Holycross Engineering College

Computer Science Engineering
Holycross Engineering College

Computer Science Engineering
Holycross Engineering College
Computer Science Engineering
Holycross Engineering College
Crop and forest fires are often detected only after they have spread extensively, as manual patrolling and satellite-based monitoring are typically slow, infrequent, or reliant on visible smoke plumes. Such delays enable small ignition points to develop into large, uncontrollable fires that devastate agricultural land, forest cover, and property. This study presents a network of solar-powered sensor towers equipped with smoke, heat, and wind-direction sensors for continuous, real-time monitoring of fields and forest zones. When fire-indicative conditions are detected, the system immediately alerts nearby farmers and local authorities via a cloud-connected communication module and simultaneously initiates an automated irrigation response in the affected zone to slow the fire's progression prior to human intervention. The system is self-sustaining through solar power, making it suitable for remote and off-grid agricultural and forest environments. By integrating embedded systems, wireless sensor networks, and IoT-based cloud alerting, the proposed approach aims to significantly reduce fire response times.
KEYWORDS
The proposed solar-powered sensor tower network offers an autonomous, always-on solution to the problem of delayed crop and forest fire detection. By integrating smoke, heat, and wind-direction sensing with cloud-based alerting and automated irrigation response, the system can significantly shorten the gap between fire ignition and containment action. Its self-sustaining solar design makes it well-suited to remote agricultural and forest regions with limited grid access. Future enhancements may include integration of machine learning-based fire classification, drone-assisted verification, and GIS-based spread prediction for large-scale forest management.
[1] A. Molina-Pico, D. Cuesta-Frau, A. Araujo, J. Alejandre, and A. Rozas, “Forest Monitoring and Wildland Early Fire Detection by a Hierarchical Wireless Sensor Network,” Journal of Sensors, vol. 2016, Article ID 8325845, 2016. [2] M. Hefeeda and M. Bagheri, “Wireless Sensor Networks for Early Detection of Forest Fires,” IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS), pp. 1–6, 2007. [3] Y. Zhang, G. Wang, and Z. Li, “Forest Fire Detection Based on Deep Learning and Image Processing,” Fire Technology, vol. 57, pp. 3011–3031, 2021. [4] S. Sharma, K. Verma, and R. Gupta, “IoT-Based Smart Irrigation System for Precision Agriculture,” International Journal of Computer Applications, vol. 179, no. 32, pp. 1–5, 2018. [5] P. Singh and A. Kaur, “LoRa-Based Wireless Sensor Network for Remote Environmental Monitoring,” IEEE Sensors Journal, vol. 20, no. 10, pp. 5623–5631, 2020. [6] T. Kim and J. Lee, “Solar-Powered IoT Nodes for Long-Term Environmental Monitoring,” IEEE Access, vol. 8, pp. 112455–112466, 2020. [7] F. Alkhatib, “A Review on Forest Fire Detection Techniques,” International Journal of Distributed Sensor Networks, vol. 10, no. 3, 2014. [8] R. Kumar and S. Das, “Smoke and Flame Detection Using CNN-Based Image Classification,” Procedia Computer Science, vol. 167, pp. 2091–2100, 2020. [9] N. Yadav and P. Rana, “Wind-Aware Fire Spread Prediction Models: A Survey,” Fire Safety Journal, vol. 120, 2021. [10] H. He and E. A. Garcia, “Learning from Imbalanced Data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, 2009.
Publication Date
7/16/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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