TreeEyed: A QGIS plugin for tree monitoring in silvopastoral systems using state of the art AI models

Abstract

Tree monitoring is a challenging task due to the labour-intensive and time-consuming data collection methods required. We present TreeEyed, a QGIS plugin designed to facilitate the monitoring of trees using remote sensing RGB imagery and artificial intelligence models. The plugin offers several tools including tree inference process for tree segmentation and detection. This tool was implemented to facilitate the manipulation and processing of Geographical Information System (GIS) data from different sources, allowing multi resolution, variable extent, and generating results in a standard GIS format (georeferenced raster and vector). Additional options like postprocessing, dataset generation, and data validation are also incorporated.

Keywords

• Tree monitoring
• Remote sensing
• Computer vision
• Deep learning
• QGIS
• Silvopastoral systems