# 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
The case for compatibility with the existing system is validated by the failure of existing systems. Traditional evidence drawn from fires shows that existing systems of sensors for fire detection often fail to detect fires early on [33], while false alarms have been reported due to defective smoke detectors and human noise. This is a critical concern given that late detection of fires complicates evacuation efforts and fire fighting. In other cases, defects in fire sensors are linked to tailored performance. Certain sensors function best with specific materials. Such challenges underscore the need to incorporate AI networks to achieve high classification rates. Cost- and poor-precision-related issues are nonexistent in probabilistic neural network (PNN)-based fire hazard simulations [33]. The simulation results show that the system had satisfactory mean classification accuracy (83-94%) in RLSSV-8 and hybrid datasets [33] (see Figure 4). The accuracy of PNN-RLSSV-8 is comparable to AI-WSN systems, which achieved a 94% accuracy following the integration of the K-nearest neighbor (k-NN) algorithm to offset the computing limitations [32]. On the downside, the accuracy of PNN systems is lower compared to combined DBN and R-LSTM-NN, which attained an accuracy of ~98%, as noted by [PERSON] et al. [16]. From another dimension, errors should not be a limiting factor to the installation of AI networks in farms, given the challenge can be resolved through pretraining and transfer learning. The low probability of accurate fire detection (35.96-65.02%) of the Haar cascade classifier machine learning algorithm was counteracted by the YOLOv3 model [11], which improved the accuracy of the machine learning algorithm through training. In other studies [90], the YOLO models proved useful in construction engineering management/structural health monitoring in combination with PCN/GAN/CNN networks. The latter evidence reaffirms the multidimensional benefits of AI networks. The utility is not confined to fire safety. [PERSON] et al. [16] observed that there were practical limits with R-LSTM-NN systems in isolation, and the synergistic benefits afforded by DBN-R-LSTM-NN systems improved the reliability of the system. The researcher is conscious of the fact that large error (nearly 100%) rates are acceptable in certain cases depending on the desired application of the model results. [PERSON] et al. [50] noted that high error rates had negligible effects in nonwind simulations involving Fire Dynamics Simulator (FDS) and computational fluid dynamics. In reality, such errors are nonacceptable in real-life settings, given they are a microcosm of the limits of the material simulations. In addition to errors, large dependence on the grid resolution of the model results and poor soot production models impact the reliability of DBN-R-LSTM-NN systems. A cost-benefit analysis of the merits and drawbacks of the technologies suggests that the practical benefits outweigh the drawbacks due to the close similarities between the simulated/theoretical and experimental values (see Table 2). On the downside, the practical use of DBN and R-LSTM-NN was confined to smart cities. There was minimal evidence of its utility in non-smart-city environments. The unique drawbacks of ANN, GA, ANFIS [16], R-LSTM-NN, and DBN networks [13; 14; 15] could be offset by pattern recognition and machine learning capabilities of AI-based systems, as noted by [PERSON] [14]. The case for machine learning systems is supported by [PERSON] et al. 's simulation of fire scenarios using large eddy simulation codes and computational fluid dynamics. CFD was proven effective in simulating the plume characteristics and large-scale effects of fires [50]. Beyond [PERSON] et al. 's research, [PERSON] et al. noted that advances in machine learning coupled with the availability of affordable computing devices with higher computing power accelerated CFD [88].