Fire risk assessment system for food and sustainable farming using ai and IoT technologies: Benefits and challenges

Abstract

Fire detection and prevention in agriculture and forestry are major challenges for food security, ecosystem preservation, and natural resource management. Forest and agricultural fires have devastating impacts on the environment, the economy, and biodiversity. Faced with this problem, the adoption of innovative technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) offers new opportunities for proactive fire risk management. This review explores the joint use of AI and IoT for fire detection, monitoring, and prevention in the agricultural and forestry sectors. The foundations of AI in agriculture are examined first, with a particular focus on machine learning and massive data processing techniques for predicting fire risk. AI applications in fire detection are then analyzed, notably through predictive models and intelligent sensor systems. At the same time, the study highlights the principles of IoT for environmental monitoring, emphasizing the role of sensors, communication networks, and cloud platforms to collect, analyze, and transmit data in real-time. Finally, the combination of AI and IoT is explored as an integrated solution for preventing fires through continuous monitoring and rapid response capability. The benefits, as well as the challenges associated with implementing these technologies, are also discussed. This review aims to provide a detailed analysis of current research, identify existing gaps, and propose perspectives for the future of agricultural and forest fire management while highlighting the importance of a technological and sustainable approach to tackling the global challenges of climate change and food security.