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@article{177467,
        author = {Jeevanantham K and Renita Pearlin T and Jessica Catherine B and Harikishan M},
        title = {AI-Driven Agricultural Field Fire Alert System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2631-2635},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177467},
        abstract = {Temperature rise and dry conditions along with human activities create an escalating risk of fire events in agricultural land areas. The system deploys an ESP32 microcontroller with flame sensor and smoke sensor and DHT sensor to monitor fire prevention and smoke level and temperature and humidity conditions in the environment. Logically distant agricultural fields host slave nodes equipped with sensors that forward obtained data through LoRa (Long Range) transmission to a central owner/master node running smoothly in remote territories. The platform ThingSpeak receives the data for continuous monitoring and analysis. Abnormal readings related to smoke levels higher than normal thresholds and temperature deviations trigger automatic email notifications to stationed farm owners or designated official entities which enable rapid response measures. Particularly ThingSpeak data serves to construct a predictive machine learning model inside MATLAB based on environmental patterns. The system's predictive function lets users prevent fires before they start thus protecting agriculture fields from destruction and enhancing operational safety. The system protects agricultural fields by combining IoT sensors, LoRa communication channels with cloud-based status monitoring and AI-based wildfire predictions at a smart cost-effective level and achieves scalability across multiple fields.},
        keywords = {Smart Agricultural Solutions, ESP32, LoRa Communication, Flame Sensor, Smoke Sensor, DHT11, ThingSpeak, MATLAB, Machine Learning, Fire Prediction, IoT Technology, Real-time Monitoring.},
        month = {May},
        }