From Pillars to AI Technology-Based Forest Fire Protection Systems

Abstract

The importance of forest environment in the perspective of the biodiversity as well as from the economic resources which forests enclose, is more than evident. Any threat posed to this critical component of the environment should be identified and attacked through the use of the most efficient available technological means. Early warning and immediate response to a fire event are critical in avoiding great environmental damages. Fire risk assessment, reliable detection and localization of fire as well as motion planning, constitute the most vital ingredients of a fire protection system. In this chapter, we review the evolution of the forest fire protection systems and emphasize on open issues and the improvements that can be achieved using artificial intelligence technology. We start our tour from the pillars which were for a long time period, the only possible method to oversee the forest fires. Then, we will proceed to the exploration of early AI systems and will end-up with nowadays systems that might receive multimodal data from satellites, optical and thermal sensors, smart phones and UAVs and use techniques that cover the spectrum from early signal processing algorithms to latest deep learning-based ones to achieving the ultimate goal.

Smart and Solar Greenhouse Covers: Recent Developments and Future Perspectives

“…In contrast to the mobile sensors embedded on greenhouse, the AI-based systems offer practical benefits in terms of flexibility, contributing to the reduction of greenhouse energy, improving yield predictability, which is integral to the sustainable production of vegetables and fruits under greenhouse environments; this in line with the previous assessments made by Castañeda-Miranda and Castaño-Meneses (2020b). The utility of IoT in commercial agriculture transcends the monitoring of agricultural structures to encompass other important facets such as intelligent hazard mitigation-fire and smoke sensors guided by AI have been developed and deployed in farms (Aspragathos et al, 2019;Su et al, 2021). From an economic perspective, the economic feasibility of IoT systems in greenhouse was contingent on whether the costs of installation and maintenance were lower and sustainable over the long term.…”

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

“…The proposals for future research were informed by the shortcomings of the BIA and the need for practical, intelligent solutions in agriculture. Even though there were diverse applications, fundamental issues of concern remain unresolved, such as the accuracy and recall ability of the BIAs relative to other alternatives such as the R-CNN [31,80,207,208]. The R-CNN was superior across different case studies because it could detect pests in complex backgrounds or the field in real-time.…”

“…The position of the pests was correctly determined because of the successful coupling with bounding box regression and the RPN module. Beyond pest detection, the R-CNN algorithm was proven effective in the performance of a wide array of functions, including fruit detection and classification [80], wild forest fire smoke detection [207], and object detection. On a positive note, the level of accuracy and precision was satisfactory because it exceeded 95%.…”

GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia

“…In the case of the real-time early forest fire prediction and detection system, the main demand is that it should be able to notify firefighters as quickly as possible in order to minimize damage to wildlife, environment, infrastructure, and people caused by the fire. In that sense, the most important requirements of the forest fire detection systems include [ 61 ]: robust continuous monitoring, fast detection of fire outbreak, determination of the exact location of fire, fast notification, and minimization of probability of false alarms occurrence. The comprehensive state-of-the-art analysis of previously proposed forest fire prediction and detection solutions shows that this system can be classified in several categories [ 61 , 62 ]: Traditional human-based observation systems; Satellite-based systems; Optical and thermal sensors (cameras)-based systems; IoT-based sensor networks and wireless sensor networks (WSN)-based systems; Unmanned aerial vehicles (UAV)-based systems.…”