# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
## 4 Conclusions  
Following the appraisal of various artificial intelligence-based satellite-based wireless sensors and optical and thermal camera systems for fire prevention/monitoring, each system has practical benefits and constraints. For example, the FSD system exhibited optimal performance in the modeling of gas dispersions through space. This means the technique could be deployed in ascertaining the explosivity limits and flammability of substances in closed environments. Such information could help guide explosion model boundary conditions to ascertain the temperature and pressure waves and changes that are linked to the explosions, among other parameters that are often disregarded in traditional models. Based on the unique benefits, it is clear that FSD offers a practical opportunity to improve fire model simulations, especially in fluid flows. However, future experiments must address current drawbacks, including higher reliance on the accuracy of the grid and grid resolution and the inability to accurately predict limited ventilation fires. The limits of FSD- and CFD-based techniques at large informed the investigation of alternative methods for AI-based fire simulation and prevention, such as the DBN-R-LSTM-NN. According to the researchers, the DBN-R-LSTM-NN method was proven useful, particularly in the prediction of the probability of fire occurrence, using data collected from various IoT devices. A key constraint was the practical feasibility of IoT data collection in non-smart-city environments such as country farms with limited integration of computing devices. The risk of fire outbreak was classified on a scale of zero to one depending on the simulated smoke, flame, temperature, FFMC, and RH values. Despite the technical challenges, the researcher noted that there were practical benefits associated with the combined DBN and R-LSTM-NN, especially in terms of the higher F-1 score scores, precision, accuracy (>98%), and marginal error rate (about 0.14%). However, the high accuracy rates in isolation cannot predict the choice of neural networks for fire safety in farm environments considering the accuracy of DBNs and R-LSTM-NNs, which is comparable to the naive Bayes classifier-FFNN system. Apart from DBNs and R-LSTM-NNs, there are other effective networks for enhancing fire safety in farms. For example, the adaptive fuzzy-RBFNN model was proven useful compared to the traditional BPNN in terms of the simulation accuracy, learning speeds, and ability to mimic human reasoning and infer prior knowledge and the experience of experts. From a long-term point of view, the cost effects, fire code limits, and lack of regulatory incentives for compliance must be addressed to improve the extent of AI system applications in farms. In theory, the cost-related barriers have been partly offset by recent advances in technology, including the development of the naive Bayes classifier-FFNN system--an efficient and cheap algorithm that has so far proven useful in the real-time detection of forest fires. Even though context-specific benefits validate the choice of DBN, R-LSTM, PNN, ANN, ANFIS, or GA, certain operational constraints are ubiquitous. For example, the theoretical assumption might not be valid in real life. Additionally, the agent might not be cognizant of the transition statistics and state space and is required to initiate action using information drawn from sensor nodes [92]. Such memoryless and reactive policy challenges are often bound to occur if the actual environment is underexplored or sophisticated and there is a limited conception of the memory dynamics. The future prospects of AI and related technologies provide a clear justification for farmers/agricultural companies to invest in advanced technologies for fire safety, despite the prevailing disregard for noncore activities. This is because smart message service, 5G networks, UAVs, IoT-SG, and decentralized energy systems in microgrids could catalyze the Agriculture 4.0 revolution and support smart cities, which are integral to agricultural efficiency.