By Blake Jackson
The Cornell Agricultural Systems Testbed and Demonstration Site (CAST) is partnering with the Weatherspoon lab in computer science to build a data pipeline and analytics platform that helps farmers consolidate their data for smarter decision-making, while ensuring privacy.
Farms generate massive amounts of data. A U.S. dairy farm, for example, collects information on herd health, breeding, and milk production. Crop-growing operations add further data, including fertilizer use, crop yields, and weather observations. Despite this wealth of information, most farms struggle to use it effectively.
“Modern farm data is highly fragmented and inconsistent,” said Yunxi Shen PhD’29, computer science. “Farmers and researchers have to spend significant time preparing the data before it is usable. Even then, it is difficult to cross-reference data collected from different sources for holistic analysis.”
CAST, comprising the Cornell University Ruminant Center, the Cornell Teaching Dairy Barn, and Musgrave Research Farm, serves as a model for U.S. agriculture. The goal is to build a data infrastructure that automates collection from various sensors such as cow biometric wearables or manure pit gas sensors stores it in a database and enables AI-driven analytics. Shen is developing a scalable pipeline to make this possible.
The database will exist both locally on farms and centrally in the cloud, allowing aggregation across multiple farms. “Early studies have shown that if I just make predictions off my own farm data, that’s not as good as if I make predictions based off of the data from ten farms,” said Hakim Weatherspoon, CAST associate director.
To maintain privacy, CAST is exploring federated learning, federated analytics, and differential privacy techniques. “CAST is the farm of the future, and in that farm of the future we want farmers to understand what’s going on with their own data and have more control over it,” Weatherspoon said.
“These techniques let farmers keep raw data on-premises for the entire duration of the process,” added Salman Abid, PhD’29. “No raw or unprocessed data leaves the farm, nor is it seen in its original form by the model provider at any time.”
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