Sensing multi-agent system for anomaly detection on crop fields exploiting the phenological and historical context

IoT technology spread led to the development of smart solutions for Precision Agriculture, employing multiple smart sensors to acquire and process data to support vegetation monitoring and crucial tasks such as seeding, irrigation, etc. to improve crop quality and production. However, the gathering of data from multiple devices requires a data integration stage that strictly depends on the context and features of the environment, including the type of environment, species, and phenology of the area considered. To this purpose, this paper introduces a multi-agent model that allows a swarm of IoT devices to perform environmental monitoring and anomaly detection on Regions of Interest (ROIs) accomplishing several tasks, including harmonization of spectral images taken from different sources, phenology data extraction about ROIs to build contextual knowledge over the ROIs and anomaly detection through vegetation index classification. A case study in a simulated real-time environment demonstrates the potential of the model to promptly alert humans about ROIs affected by critical vegetation, burned areas, and ROIs that can be at risk after critical events occurred in their surroundings.