A multi-agent knowledge-enhanced model for decision-supporting agroforestry systems

Precision Agriculture (PA) and Forest Management (FM) applications require sensor-based environment monitoring to assess the vegetation status of monitored areas. Vegetation Indices (VIs), assessed from satellite-taken spectral images, depict some features (e.g., vegetation vigour, coverage, etc.) but they are not enough to describe vegetation status, hence they need to be contextualized according to the area phenology, latitude and weather for correct vegetation status interpretations. Moreover, heterogeneous data collection can cause data integration and interoperability issues. Additionally, human operators, who have to monitor multiple vast environments in time critical contexts, require brief meaningful reports about occurred situations. In this paper a knowledge-based multi-agent approach is presented to deal with environment monitoring of user-specified Regions of Interest (ROIs) and assess their vegetation status. The approach employs different types of agents to carry out various tasks, including data acquisition and knowledge storing, end-user interaction and vegetation analysis accomplishment. The end-user can request different types of analysis and pass data to the system through an agent-managed GUI, hence vegetation analysis is carried out by using a decision tree-based method to properly query the KB built on VIs and contextual data to consequently build a report about the vegetation status of the ROI. The built report includes a description of other features (soil, weather) that helps depicting the detected vegetation status. Several case studies demonstrate the functioning and efficacy of the approach.