Harvest is a tool designed to allow multiple types of stake holders in the digital agriculture space further their own unique goals from research to increases of the bottom line. Harvest allows for the creation of pipelines where users can preprocess their data, train models on HPC resources, infer on models to get insights on farm fields, and some visualizations to give an at a glance understand of what is happening on the field.
The ML Field Planner is a framework for analyzing ML pipelines and studying edge-to-center tradeoffs regarding function placement of ML. Using ML Field Planner, researchers configure experiments to run on real IoT hardware, configure machine learning models to analyze custom benchmark datasets, and experiment with different algorithm configurations, such as storage compression, all from a graphical user interface.
A field-deployable multimodal data infrastructure that integrates autonomous sensing, edge computing, and drone-based platforms to address critical data acquisition challenges in animal ecology research.
The Food Equity Access Simulation Technology (FEAST) tool — previously known as the Food Access and Strategy Simulation (FASS) tool — is a powerful platform for analyzing how changes in the food retail landscape, such as adding or removing stores, affect household food access. FEAST enables users to simulate and evaluate strategies aimed at improving food equity across communities.
This project provides an open-source orchestration engine designed to automate and scale the transformation of raw Unmanned Aerial Systems (UAS) imagery into structured, AI-ready datasets for agricultural research.