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AgAID Digital Agath0n

January 27 @ 4:00 pm - January 29 @ 5:00 pm

The Digital Agathon

Graduate and undergraduate students with an interest in Computer Science and Agriculture are welcomed.

Using the resources and mentors we provide, you have an opportunity to explore what technology can do for agriculture.

Join us for ~48 hours to level up your AI and digital ag skillset and for the opportunity to win cash prizes!


  • Register by: Monday, January 9, 2023
  • Selected participants notified: January 13, 2023
  • Teams will be assembled mid-January
  • Digital Agathon Event: January 27-29, 2023
  • Awards Ceremony: January 30, 2023


  • Students enrolled at US-based Universities/Colleges from all years of undergraduate and graduate school are eligible to participate.
  • Priority given to students affiliated with the AgAID Institute.
  • Students with a range of computer science skills and agricultural interest or experience are encouraged to apply.

The Challenges

Computer Vision for Localizing and Counting Apples

Growing and harvesting tree fruits like apples require significant intellectual and physical labor. Growers must make many decisions throughout the process about orchard organization, pruning strategies, watering and fertilization schemes, blossom thinning, and harvest scheduling. Answering these questions ultimately relies on being able to accurately measure the impact of different choices over time by assessing fruit yield. However, measuring the number of apples grown in experimental plots can be a time-consuming and error-prone process during the fast-paced harvest season. Harvesting itself also requires significant human labor to remove fruits from trees efficiently and without damaging either the fruit or the trees themselves.

Forecast total cool-season precipitation for the Sacramento drainage basin in California

You will be developing and applying models to forecast cool-season (Nov-Mar) precipitation in California (in the Sacramento Basin) using existing geospatial data. The goal is to create novel approaches to enhance seasonal forecasting, which can in turn reduce risks to our water systems and help water managers efficiently manage hydrological regimes. Existing data include patterns of sea surface temperature, climate indices that represent atmospheric circulation regimes, soil moisture, and sea ice leading up to the forecast period. You will be developing models based on observations for the 1980-2011 period, and one element of evaluation will be validation based on data for 2012-2022.


January 27 @ 4:00 pm
January 29 @ 5:00 pm