Zhaodan Kong

This project aims to develop AgML, an open-source Python library to accelerate agricultural AI by providing access to ML datasets, benchmarks, pre-trained models, workflows, and synthetic data generators. It builds on the successes of the Year 3 AIFS proposal, "AgML: Open-Source Infrastructure to Accelerate Scaling of Agricultural AI Technologies."

Recent studies and commercial applications focus on automated food identification for calorie and macronutrient estimation. However, existing models trained on internet-based recipes may struggle in real scenarios, prompting the project to develop an explainable diet recommendation system that considers fine-grained information like ingredients for accurate dietary analysis.

The project aims to build infrastructure for the rapid scaling of agricultural AI technologies by creating an open-source Python library, AgML, providing access to agricultural-specific machine learning datasets, benchmarks, pre-trained models, workflows, and synthetic data generators, building on successes from the Year 2 AIFS proposal.