# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
## 2 Fire Safety in Agricultural Structures/Farms
### Future Prospects of AI in Agricultural Structures and Beyond
Despite the latter progress, non-CFD=based models based on computational models are highly preferred due to the complex geometries (associated with accounting for convective and conductive heat transfer, bluff-body aerodynamics, multiphase flow, turbulent mixing and combustion, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Comparison between Pool Fire Data and FDS Model**} & \multicolumn{3}{c}{**Comparison between Pool Fire Data and FDS Model**} \\ \multicolumn{2}{c}{**Predictions—Case 1, Wind**} & \multicolumn{3}{c}{**Predictions—Case 2, No Wind**} \\ \hline Distance & Experimental & Model prediction & Distance & Experimental & Model prediction \\ to target & approximations (kW/m\({}^{2}\)) & (kW/m\({}^{2}\)) & to target & approximations (kW/m\({}^{2}\)) & (kW/m\({}^{2}\)) \\
50 & 32 & 31.13 & 50 & 8 & 4.54 \\
75 & 17 & 13.06 & 75 & 4 & 2.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the FDS model predictions and the actual fire data in the wind and nonwind scenarios [50]. Figure 4: Classification accuracy of PNN networks with IAQ datasets [33]. radiative transport), turbulence, assumptions, and context-specific benefits of different CFD programs [50]. Anecdotal evidence suggests traditional farm structures might not have ample resources to invest in CFD systems for fire prevention, given that the allocation of resources to nonproductive activities is often de-emphasized [23]. Additionally, there are no commercial on-demand service providers. The inability to invest in AI-based systems amplifies the risk of catastrophic fire hazards. The current discussion has practical implications on the safety of farm/agricultural structures, given it provides compelling insights on the suitability of various fire-retardant materials such as liquid hydrocarbon resin, thermoplastic acrylic resin, melamine, pentaerythnitol, and ammonium polyphosphate, which are susceptible to char cracking and form detachment. Beyond the technological aspects of fire-retardant materials, the application of artificial intelligence in smart buildings has economic benefits over the long term. Wildfires in the state of Texas cost about USD 21 million (this includes damage to animal feeds/pasture, death of livestock). This is according to the Texas A&M AgriLife Extension Service [35]. The phenomenon is not unique to the US. The Food and Agriculture Organization (FAO) estimates that commercial and small-holder agriculture is vulnerable to natural disasters, including wildfires. Recent fires have burned vast tracts of land, including farms across Europe, and Greece remains one of the most vulnerable states [31]. Between 1997 and 2007, wildfires in Greece and Indonesia burned 670,000 acres and 8 million hectares, respectively, and resulted in more than 300 deaths [91]. Recently, at least USD 10 billion was lost through fire damage across the US [87] and the UK [51]. The cost of human lives could be offset by investment in intelligent systems for fire safety. From an economic perspective, wild and human-induced fires are unsustainable given they accounted for 1% of the total production losses on global agriculture, particularly in Asia, Latin America and the Caribbean, and Asia between 2005 and 2015. This is equivalent to more than USD 3 billion in direct economic losses [36], which, in turn, have a long-term impact on the global food safety systems. The significant economic losses provide sufficient incentive for engineers, scientists, and owners of commercial farms to invest in the next generation of AI-based fire-prevention materials, especially in Europe and Asia. The need for AI networks and WSN is supported by global fire statistics. No major fire has been documented in the US since 1871 and 1894 Peshtigo and Great Hinckley Fires in Wisconsin and Minnesota, which claimed nearly 3000 lives [91]. The contrary is true in Asia, Southern Europe, Latin America, and Africa.