Yufang Jin

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.

The project involves collaboration on open-source infrastructure development, optimized sensor-driven precision agriculture, and inexpensive soil nitrate sensors, with the potential to accelerate agricultural AI technologies through AgML Python library and to develop tools for autonomous vehicles and soil nitrate measurement.

Enhancing yield forecasting for specialty crops, such as strawberries, grapes, and almonds, involves deploying monitoring sensors, developing a synthetic data generation pipeline linked to a 3D crop model and deep learning, and creating a framework that fuses multi-modal ground-based and aerial data to predict yield.