
Year 3 Research Project
Open-Source Infrastructure to Accelerate Scaling of Agricultural AI Technologies
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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”.
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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
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Publications

Journal Article ⏐ Plant Phenomics 2023Journal Article ⏐ Plant Phenomics 2023
Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models
DOI: 10.34133/plantphenomics.0084

Journal Article ⏐ Computeres and Electronics in Agriculture 2022Journal Article ⏐ Comput. Electron. Agric. 2022
A Comprehensive Review of Remote Sensing Platforms, Sensors, and Applications in Nut Crops
Jafarbiglu, Hamid,and Alireza Pourreza
DOI: 10.1016/j.compag.2022.106844

Journal Article ⏐ ISPRS Journal of Photogrammetry and Remote Sensing 2023Journal Article ⏐ ISPRS J. Photogramm. Remote Sens. 2023
Impact of Sun-view Geometry on Canopy Spectral Reflectance Variability
Jafarbiglu, Hamid,and Alireza Pourreza
DOI: 10.1016/j.isprsjprs.2022.12.002

Journal Article ⏐ Computeres and Electronics in Agriculture 2023Journal Article ⏐ Comput. Electron. Agric. 2023
Early Almond Yield Forecasting by Bloom Mapping Using Aerial Imagery and Deep Learning
Chakraborty, Momtanu,Alireza Pourreza,Xin Zhang,Hamid Jafarbiglu,Kenneth A Shackel,and Theodore DeJong
DOI: 10.1016/j.compag.2023.108063