The proposed open-source infrastructure development project has the potential to accelerate the scaling of agricultural AI technologies across academia and industry. Currently, no such framework exists for agricultural AI, but similar frameworks are being successfully developed in the medical software community, for example. The proposed open-source Python library, called AgML, will provide data and code resources to academic and industry ML developers, ultimately aiming to build a broader open-source community through shared infrastructure.
This project aims to develop infrastructure to accelerate the scaling of agricultural AI technologies. Specifically, we 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 and integrates two Year 1 AIFS proposals (AI Enabled Yield and AI-Enabled Nitrogen & Water), and reorganizes the proposals under the Year 2 RFP
C. Simmons, X. Liu, N. Nitin, A. Arias, J. Evans, X. Chen, C. Shao, M. Stasiewicz, Collaborator: E. Ligon