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Year 2 Research Project
Improving Tomato Quality and Safety by AI-Driven Supply Chain of Optimization and AI Prediction of Losses at Specific Stages

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Description

SIGNIFICANCE

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.

GOALS

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

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Photos

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Team

Portrait of Christopher Simmons

Christopher Simmons

Principal Investigator

Portrait of Xin Liu

Xin Liu

Principal Investigator

Portrait of Nitin Nitin

Nitin Nitin

Principal Investigator

Portrait of Ana Arias

Ana Arias

Principal Investigator

Portrait of Chenhui Shao

Chenhui Shao

Principal Investigator

Portrait of Matt Stasiewicz

Matt Stasiewicz

Principal Investigator

Portrait of Ethan Ligon

Ethan Ligon

Collaborator