Open-Source Infrastructure to Accelerate Scaling of Agricultural AI Technologies
Description
This project aims to develop infrastructure to accelerate the scaling of agricultural AI technologies. Specifically, researchers will create an open-source Python library, called AgML, that enables access to agricultural-specific machine learning (ML) datasets, benchmarks, pre-trained models, workflows, and synthetic data generators. This work builds off numerous successes from Year 2 AIFS proposal “AgML: Open-Source Infrastructure to Accelerate Scaling of Agricultural AI Technologies”.
Team
Mason Earles
Principal Investigator
Zhaodan Kong
Co Principal Investigator
Brian Bailey
Co Principal Investigator
Stavros Vougioukas
Co Principal Investigator
Isaya Kisekka
Co Principal Investigator
Alireza Pourreza
Co Principal Investigator
Yufang Jin
Co Principal Investigator
Mark Mueller
Collaborator
Francesco Borrelli
Collaborator
Tarek Zohdi
Collaborator
Christine Diepenbrock
Collaborator
Publications
Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models
A Comprehensive Review of Remote Sensing Platforms, Sensors, and Applications in Nut Crops
Impact of Sun-view Geometry on Canopy Spectral Reflectance Variability
