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TreeEyed QGIS Plugin

A QGIS plugin for tree monitoring using AI.

Simple Inference

Features

This plugins seeks to integrate existing and custom AI models for tree monitoring (semantic segmentation, instance segmentation, and object detection) in high resolution RGB imagery.

Apart from the model handling this plugin facilitates the integration with QGIS layers for image extraction and post-processing. Additional features for dataset creation and validation in COCO format are available.

Model Source Preferred spatial resolution
HighResCanopyHeight https://github.com/facebookresearch/HighResCanopyHeight 1 m
Mask R-CNN Custom trained 4.77 m
Deepforest https://github.com/weecology/DeepForest less than 0.5 m

Installation

TreeEyed plugin is now available directly in the QGIS Python Plugins Repository and can be installed using the plugin manager in QGIS.

Plugin Install

Documentation

Documentantion and tutorials are available here.

Requirements

This plugin works on QGIS, and it was tested on Windows using QGIS 3.28.9-Firenze.

It requires additional python packages that can be installed by using the plugin and following the installation instructions:

  • rasterio
  • pycocotools
  • torch
  • torchvision
  • opencv-python
  • deepforest

A dependencies folder with the required packages will be added in the plugin root folder.

License

This repository is licensed under the Apache 2.0 license.

Authors

Tropical Forages Team

Alliance Bioversity International & CIAT

Research

A. F. Ruiz-Hurtado, J. P. Bolaños, D. A. Arrechea-Castillo, and J. A. Cardoso, ‘TreeEyed: A QGIS plugin for tree monitoring in silvopastoral systems using state of the art AI models’, SoftwareX, vol. 29, p. 102071, Feb. 2025, doi: 10.1016/j.softx.2025.102071.

Citation (Bibtex format):

@article{ruiz-hurtadoTreeEyedQGISPlugin2025,
  title = {{{TreeEyed}}: {{A QGIS}} Plugin for Tree Monitoring in Silvopastoral Systems Using State of the Art {{AI}} Models},
  author = {{Ruiz-Hurtado}, Andres Felipe and Bola{\~n}os, Juliana Perez and {Arrechea-Castillo}, Darwin Alexis and Cardoso, Juan Andres},
  year = {2025},
  month = feb,
  journal = {SoftwareX},
  volume = {29},
  pages = {102071},
  issn = {2352-7110},
  doi = {10.1016/j.softx.2025.102071},
  keywords = {Computer vision,Deep learning,QGIS,Remote sensing,Silvopastoral systems,Tree monitoring},
}