GIS Workflow for Digital Agriculture

Building the spatial data behind AgTwin

Agronomeye's AgTwin is a 3D digital twin platform that brings together detailed information about farms to support planning and decision-making. Behind the 3D models is a large amount of spatial data that needs to be captured, processed and managed consistently.

Mammoth has worked alongside the Agronomeye team over several years, helping develop the geospatial workflows and data infrastructure that underpin AgTwin.

Building a reliable data workflow

Capturing detailed farm assets at the scale and level of accuracy required by AgTwin presented some interesting GIS challenges. Together with Agronomeye, we developed a systematic approach to data capture, processing, and validation.

QGIS sits at the centre of the workflow, with custom templates and built-in validation helping teams capture data consistently. Automation through QGIS plugins and PyQGIS scripts takes care of repetitive processing and validation, while Bash, Python and GDAL workflows handle imagery and elevation data.

The resulting datasets are stored and managed through PostGIS, providing a structured spatial database for the growing collection of farm data.

Supporting the detail behind the bigger picture

Mammoth's work has included elevation model and imagery processing, hydrological modelling, PostGIS database design, and workflow development.

The focus has always been on making the underlying GIS work reliable and repeatable, so that Agronomeye can turn detailed farm data into the digital environments at the heart of AgTwin.

It's been an ongoing collaboration, with Mammoth working alongside the Agronomeye team as the platform and its data requirements have evolved.