# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
###### Abstract  
Artificial intelligence applications in fire safety of agricultural structures have practical economic and technological benefits on commercial agriculture. The FAO estimates that wildfires result in at least USD 1 billion in agriculture-related losses due to the destruction of livestock pasture, destruction of agricultural buildings, premature death of farm animals, and general disruption of agricultural activities. Even though artificial neural networks (ANNs), genetic algorithms (GAs), probabilistic neural networks (PNNs), and adaptive neurofuzzy inference systems (ANFISs), among others, have proven useful in fire prevention, their application is limited in real farm environments. Most farms rely on traditional/non-technology-based methods of fire prevention. The case for AI in agricultural fire prevention is grounded on the accuracy and reliability of computer simulations in smoke movement analysis, risk assessment, and postfire analysis. In addition, such technologies can be coupled with next-generation fire-retard materials such as intunnecent coatings with a polymer binder, blowing agent, carbon donor, and acid donor. Future prospects for AI in agriculture transcend basic fire safety to encompass Society 5.0, energy systems in smart cities, UAV monitoring, Agriculture 4.0, and decentralized energy. However, critical challenges must be overcome, including the health and safety aspects, cost, and reliability. In brief, AI offers unlimited potential in the prevention of fire hazards in farms, but the existing body of knowledge is inadequate. artificial intelligence; agricultural structures; fire safety; neurofuzzy inference system; fire-retardant materials +
Footnote †: journal: Applied  
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inference systems (ANFISs) [3], and the deployment of backpropagation methods. Recently, a three-layer backpropagation neural network was proven useful by [PERSON] et al. [4] in determining the limiting temperature of steel planar tubular truss exposed to fire. Currently, the accuracy and ability of sensors to detect fires accurately depend on the proximity between the fire and the sensors [5; 6; 7; 8; 9; 10]. If the threshold distance is exceeded, a false result might be generated [11]. This challenge has been partly resolved by AI techniques, which facilitate the detection of fires over wide ranges. Alternatively, vision-based sensors might be integrated to provide better warning [12]. This review builds upon existing findings on evacuation modeling for agricultural structures [13], progress made in AI-based fire evacuation of timber structures [14], and the utilization of genetic algorithms (GAs), an adaptive neurofuzzy inference system (ANFIS), and an artificial neural network (ANN) to model the fire regardless of wall coatings [13; 14; 15]. Other researchers have demonstrated the practical usefulness of recurrent LSTM neural networks (R-LSTM-NNs) and deep belief networks (DBNs) in real-life case studies of fire hazard prevention using IoT in smart cities [16; 17; 18; 19]. The case for smart cities was supported by [PERSON] et al. [20], who noted that smart sensors combined with AI networks were indispensable in environmental monitoring [20]. The unique benefits and drawbacks associated with DBNs, ANNs, and other AI networks are critiqued from the perspective of user function/management, information management, basic operation, emergency evacuation, and intelligent fire protection [21]. A greater emphasis was placed on the unique benefits afforded by AI systems in terms of autonomous system function, queries, and path planning (see Figure 1).