# 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
Artificial-Intelligence-Based Wireless Sensor Network (WSN)-Based Systems for Fire Prevention/Monitoring  
WSN coupled with IoT has so far proven useful in continuous monitoring of fire risk in forests [31] through real-time animal tracking and autonomous fire detection. The risk of errors in such systems is often offset by the integration of fuzzy-logic-based models to counteract false alarms, while the costs of integrating the sensor systems are addressed through the model for optimal distribution of sensors over large tracts of land and energy efficiency [31]. Despite the strong case for integrating AI into WSNs made by [PERSON] et al. [31] and [PERSON] et al. [30] and other researchers, certain drawbacks must be taken into account. [PERSON] et al. [32] claimed that AI networks contributed to the overheating of WSNs in the transmission of computing data. The WSNs are also burdened by fuzzy logic and decision trees, a factor that might impact accurate and valid decision making in cases involving large datasets with multiple components. The drawbacks listed above show that the computational and memory requirements associated with AI are magnified by the potential impairment of WSNs due to overheating and related effects. The potential installation of WSN-supported AI networks in farms/agricultural settings is further complicated by the demanding maintenance and installation process [31], unsatisfactory communication speeds, temperature-dependent accuracy (see Figure 5) [32], and limited energy capacity, which, in turn, translate to higher costs of maintenance and reduced performance. The latter issues can be seen as a characteristic case of the tradeoffs implied by the increase in complexity while implementing modern agricultural applicationsand require meticulous studying and possibly the adoption of a scalable WSN architecture, in terms of computational load, communication quality, cloud-edge workload balance, and energy efficiency, for efficiently addressing the critical sensing and acting functions [92]. It is encouraging that new modules are appearing either assisting existing systems in performing AI-related operations (i.e., the Intel NCS2 unit [93]) or acting as standalone units compatible with comparatively easy-to-use platforms for AI model training [94]. As noted by [PERSON], farmers and agricultural investors often place lesser emphasis on fire safety and other nonproductive activities [23]. This means the AI networks should be affordable and functional to appeal to budget-conscious farmers/agricultural companies. Because existing technologies have not resulted in the development of energy-efficient and self-sustaining WSN-supported AI networks suitable for farms/agricultural settings, it is anticipated that the widespread use of artificial intelligence-based wireless sensor network (WSN)-based systems for fire prevention/monitoring would be limited at least in the short term. Even if the energy issues are addressed, users have to contend with system errors. Two false alarms were documented by [PERSON] et al. [32] out of 11 real-time measurements taken between 13 and 23 March 2020 [32]. The error rates are unacceptable in commercial agricultural settings where fire damage could pose unquantifiable damage to livestock, farm equipment, agricultural structures, and farm produce/stored grains and where the risk of network errors is amplified by extended use. Alternatively, users might opt to integrate K-NN algorithms to enhance the processing/computing capabilities of WSNs in farms. Artificial-Intelligence-Based Optical and Thermal Cameras Based Systems for Fire Prevention/Monitoring  
The AI-based optical and thermal cameras offer practical benefits compared to WSN systems for fire prevention, which are impaired by the greater risk of false alarms, computing limitations, unsatisfactory energy efficiency, demanding installation, and scenario-specific applications [11; 32; 39].