Akademik Veri Yönetim Sistemi
Scientific African, cilt.27, ss.2559, 2025 (ESCI)
Food security has become a major concern for most countries. This is due to: 1) the growth of the world population, 2) the decline of natural resources, 3) the loss of agricultural land, and 4) the increase of unforeseen environmental conditions (storms, fires, and other natural hazards). The fire outbreak, in general, has developed into a serious concern. In the coming years, the rate of fire outbreaks could rise exponentially, requiring immediate attention to avoid loss of property and life. To resolve such an issue, a shift from the agricultural industry to smart agriculture via applications of 1) the Internet of Things (IoT), 2) embedded systems, and 3) sensors for fire prevention are required to improve operational efficiency and productivity. A fire detection and anti-fire security (FDAS) system in smart agriculture using the IoT and embedded system is proposed. The proposed system has four technology levels: 1) the edge network layer, 2) the fog network layer, 3) the cloud computing layer, and 4) the data representation layer. The proposed system uses an embedded system like a Raspberry Pi device and sensors to measure the amount of fire smoke in the air and the proportion of fire in the area. The data obtained from the sensors are sent over the internet to the ThingSpeak platform using the machine-to-machine-based Message Queuing Telemetry Transport (MQTT) protocol for further display and analysis. Data available is then 1) stored, 2) processed, and 3) visualized through the ThingSpeak platform in real-time. An e-mail alert is sent to the farm owner if a fire is detected on the farm using the Simple Mail Transfer Protocol (SMTP). When a fire is detected, the anti-fire system is activated. It further filters and analyzes the data using the MATLAB application. Python programming language is also used to develop the program source code. The performance results of the proposed scheme show an accurate fire detection and anti-fire system performance for improving food security and sustainability in agriculture.