# 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
In brief, investments in AI systems would be capital intensive in the short term, but future benefits outweigh the costs because agriculture would become more cost effective for farmers [84] and less strenuous on the environment. The Cost Benefits of Genetic Algorithms (GAs), Adaptive Neurofuzzy Inference System (ANFISs), and Artificial Neural Network (ANNs), Probabilistic Neural Network (PNNs), Recurrent LSTM Neural Networks (R-LSTM-NNs), and Deep Belief Networks (DBNs)  
The case for AI- and IoT-based networks for fire prevention is supported by the complexity of fire and evacuation procedures and the failure of traditional mechanisms. Traditional guidelines indicate that the risk of fire hazards in livestock barns could be mitigated by the installation of approved fire doors, the incorporation of fire-resistant materials and coatings such as latex, reducing the number of stalls between the points of entry and exit, the integration of autonomous functioning sprinkler systems, fire, and smoke detection systems [85], and an ample supply of water [21; 22]. This is in line with the silo fire decision tree [85]. On the downside, there is no guarantee that compliance will help prevent fires. [PERSON] [21] notes that evacuation is hampered in most cases due to the peculiarities of human behavior, which increases the difficulty of regulating the spread of fires. Human cognition in isolation cannot accurately predict when it is most appropriate to deploy fire-fighting equipment in buildings while preserving the integrity of function and minimizing potential damage to the equipment and materials preserved in the building. Given the limits of human cognition and traditional planning methods, artificial intelligence technologies have proven useful in simulating fire spread, relocation complexities in high-rise buildings, and best-case scenarios for relocation. The accuracy of intelligent technologies is reinforced by the assimilation of complementary technologies, such as GIS [86], satellite lightning imaging sensor (LIS)/optical transient detector (OTD), statistical machine learning [87], and CFD. Presently, machine learning has accurately predicted fire ignition caused by lightning [24]. This was facilitated by LIS/OTDs. Model data and experimental evidence have demonstrated that recurrent LSTM neural networks (R-LSTM-NNs), deep belief network (DBNs), genetic algorithms (GA), adaptive neurofuzzy inference systems (ANFISs), and artificial neural networks (ANNs) offer unique benefits in the generalization of real-world objects [16; 47; 50; 56; 88]. However, the networks offer localized benefits in error rates, recall, accuracy, and precision. Certain challenges apply to most ANN-based systems, including the ability to quantify the prior knowledge and experience of the engineers/experts into data inputs to mitigate subjectivity [47]. This problem has been partly resolved by incorporating fuzzy logic [12] and the development of combined networks. For example, according to [PERSON] et al. [16], R-LSTM-NN and DBN networks were better compared to ANN and other networks based on the superior F-1 score scores, marginal error rate (~0.14%), recall, accuracy (~98%), and precision. Beyond combined networks, [PERSON] and [PERSON] [1] argued that expert thinking could be conveyed through fuzzy set levels, which are defined by \"if-then\" rules. The intelligent systems' ability to quantify expert knowledge is critical in driving uncertainties in different control mechanisms for precise outcomes. The proposals for rule-based algorithms were supported by [PERSON] et al. [89]. The accuracy rates of DBNs and R-LSTM-NNs are similar to the naive Bayes classifier-FFNN system, whose accuracy was estimated to be 98.67% [30]. Considering that different neural networks might possess comparable levels of accuracy, the choice of AI techniques for agricultural fire detection in wireless sensor networks [46] should be guided by other considerations, including cost, learning capabilities, compatibility with support networks, and precision.