Intelligent Monitoring and Early Warning System Against Frosts for Sustainable Agriculture

Abstract

Frosts are an adverse climatic phenomenon that severely affects agriculture, causing irreversible damage to plants. This process leads to dehydration, deterioration of plant tissue, and, in extreme cases, total crop loss. The lack of effective prevention and response strategies exacerbates its consequences, resulting in significant economic losses, particularly among small-scale farmers. In Ecuador, the Andean region is especially vulnerable, as its agricultural production depends on stable climatic conditions to achieve adequate harvests. This study presents the development of an early warning system against frosts, based on an Internet of Things (IoT) architecture, which employs long-range Wireless Sensor Networks (WSNs) and supervised learning algorithms specifically decision trees to anticipate frost-prone conditions and issue timely alerts to farmers. This enables the adoption of preventive measures that minimize agricultural losses and strengthen the sector’s resilience to extreme weather events. The research was conducted using the Action Research methodology, which involved an analysis of the phenomenon and the requirements of the stakeholders, followed by the design of a solution that included the selection of appropriate hardware and technologies, as well as the validation of technical requirements. The system was designed and implemented in a real agricultural environment, and the results demonstrated that the proposed solution is reliable, accurate, and adaptable, enabling timely frost detection and real-time alert generation. Furthermore, its effectiveness in crop protection was evident, contributing to the development of a more efficient, sustainable, and technologically advanced agriculture.