SIGNIFICANCE

The three teams on this project are collaborating in the areas of open-source infrastructure development, optimized sensor-driven precision agriculture, and novel inexpensive wireless sensors for accurately measuring soil nitrate. 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. 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. The proposed optimized sensor-driven resilient precision agriculture component of the project could ultimately result in decreasing economic damage from airborne delivery of agricultural products. Additionally, the addition of inexpensive soil nitrate sensors could reduce cost and labor involved in measuring soil nitrate levels.

GOALS

AgML’s objectives are to centralize and standardize datasets, develop benchmarks and pre-trained models, create ag-specific ML workflows, and generate synthetic ML agricultural data. The optimized sensor work will provide useful tools to enable rapid path planning for autonomous vehicle operators in real-time and to train operators in large surface area food systems. The soil nitrate sensors will be developed and characterized in real world conditions.