article · Results in Engineering
Agricultural systems face rising pressures from climate change, making early hazard detection vital for safeguarding crops and food security. An integrated internet of things system offers an approach for early fire detection in smart farming environments. The framework relies on a three-tier architecture covering an IoT device layer, a cloud layer, and an application layer. Physical smoke and flame sensors link to a Raspberry Pi for local data processing, with environmental data streamed to the ThingSpeak cloud platform for storage. Combining ThingSpeak with MATLAB visualisation tools enables continuous real-time monitoring and detailed trend analysis of historical conditions. The setup delivers improved responsiveness and accuracy in detecting fires, supporting sustainable agricultural risk management by mitigating potential crop destruction.
Climate change intensifies risks to natural resources and food safety, making crop destruction from agricultural fires an urgent threat. Providing reliable, real-time fire detection helps farmers intervene before blazes spread. This protective measure reduces economic losses, supports continuous food production, and strengthens the overall resilience and sustainability of agricultural systems facing severe environmental challenges.
The system applies directly to smart farming and agricultural risk management to protect crops and farming infrastructure. Prospective users include agricultural operators and farm management enterprises seeking real-time hazard detection. Based on the integration of specific hardware and cloud software to produce tested results, the system appears to be an applied prototype, though the abstract does not describe a commercial manufacturing or distribution pathway.
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• Integration of smoke and flame sensors with Raspberry Pi and ThingSpeak for advanced fire detection. • Three-tier architecture - IoT, cloud, application - for effective monitoring. • Use of ThingSpeak and MATLAB for in-depth data analysis and visualization. • Increased responsiveness and accuracy in fire detection, reducing crop damage. • Contribution to more sustainable agricultural risk management and enhanced food security. The integration of internet-of-things (IoT) technologies is proving crucial for optimizing crop management and monitoring in smart farming. The challenges posed by climate change which involve 1) increased pressure on natural resources, and 2) heightened food safety requirements, eventually call for innovative solutions to protect agricultural resources. Early fire detection, in particular, is essential to prevent major damage and ensure the sustainability of agricultural practices. This article presents an advanced IoT architecture for early fire detection. The proposed architecture integrates 1) smoke and flame sensors, 2) a Raspberry Pi for local processing, and 3) the ThingSpeak platform for data storage and visualization. The system is based on a three-layered architecture: 1) the IoT device layer, 2) the cloud layer, and 3) the application layer, enabling real-time collection and analysis of environmental data. Sensors collect real-time environmental data, which is then transmitted to ThingSpeak for storage. The ThingSpeak platform, combined with MATLAB visualization tools, enables continuous monitoring and in-depth trend analysis of historical data. The results show a clear improvement in accuracy and responsiveness in fire detection while contributing to the safety of agricultural resources and more sustainable management. The blend of IoT technologies, real-time data processing, and cloud-based visualization in this proposed detection system represents an important step towards safer and more resilient agriculture in the face of environmental risks.
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DOI: 10.1016/j.rineng.2024.103392
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