# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
## 2 Fire Safety in Agricultural Structures/Farms
This might be achieved using heating, ventilation, and air conditioning (HVAC) with climatic, user, and grid response capabilities [43]. A key issue that should be addressed moving forward is whether it is feasible for AI networks to detect the irregular installation of electrical equipment, which is a risk factor for fires in buildings [44]. The risk of such fires is exacerbated by the spontaneous combustion of hygroscopic materials. Despite the evidence to the contrary, the significant costs associated with fires do not provide sufficient incentives for farmers to invest in fire-prevention systems, considering that such events are isolated [31; 36], and there are no binding legal requirements. Nearly all farms across the developed world are not bound by national or regional fire-prevention codes [23]. Despite the challenges, the case for fire prevention is validated by the intrinsic benefits of preventing loss of life and loss from a business perspective considering the cost of fires is enormous. The cost of fire damage should incentivize stakeholders to engage in voluntary preventative action. The growing demand for fire-retardant materials has practical implications on the design of finishing materials with customized performance profiles for better flame retardance (coating formulations and architectural appearance). The case for fire-retardant materials has been made by different experts, including [PERSON] et al. [22]. This is despite the fact that such systems do not offset the potential risk associated with highly flammable substances such as fertilizers, hay and straw, pesticides, and paint. Nonetheless, some progress has been made through research and development (R&D). Recent R&D has led to the development of intumescent coatings, which satisfy the new criteria for fire retardance [15]. The superiority of the new class of coating materials is interlinked with the structural and chemical features of the coatings. The coating features three components, namely polymer binder, blowing agent, carbon donor, and acid donor. Each component serves a unique function. The primary function of the polymer binder is to hold the acid donor, carbon donor, and blowing agent in position. The acid donor guides the decomposition of mineral acid for the subsequent development of carbonaceous char, while the form expansion process is augmented by the macromolecular binder [15]. However, this class of coatings does not satisfy the diverse market demands, especially from a cost and technology perspective. In theory, the application of intumescent coatings and their ability to form insulation barriers on substrates exposed to fires is impacted by the response timelines [2] and the incompatibility of molecular simulations in making a distinction between nongenuine fire phenomenon and the real fire in its incipient stage. Non-AI-based materials have long response timelines, which, in turn, impact the reliability of such materials. This informs the need to integrate computer simulations and AI for better risk assessment, smoke movement analysis, and failure analysis [45]. The claims made by [PERSON] et al. about the suitability of AI-based materials in fire prevention are consistent with [PERSON] et al. 's investigation of the suitability of AI in forest fire protection systems [31]. In the latter case, AI systems were grouped into four broad classifications, including optical and thermal cameras for fire prevention, wireless sensor networks [46], satellite-based systems, and human-based observational systems. The support for AI-based system by [PERSON] et al. and [PERSON] et al. [31; 45] negates the fact that fire safety engineering and evacuation modeling is a multidisciplinary undertaking that integrates elements of behavioral science (sociology and psychology), data analytics (incorporating aspects of physics, mathematics, and computer science), and other technical domains.