A team from Stanford University won the first Syngenta Crop Challenge in Analytics from Syngenta and the Analytics Society of The Institute for Operations Research and the Management Sciences (INFORMS).
The group, whose entry was entitled “Hierarchy modeling of soybean variety yield and decision making for future planting plan,” includes Xiaocheng Li, Huaiyang Zhong and associate professors David Lobell and Stefano Ermon. The project modeled a system for predicting soybean seed variety selection.
“It has been a wonderful experience working with Syngenta on this project, and we are excited about the impact our work can have on improving crop yields and addressing food security challenges,” Li said. “Operations research and advanced analytics can contribute to variety development and evaluation, reducing costs and improved efficiency. Extracting useful insights from massive, unstructured datasets informed our findings and proves to us there is a lot of potential for modern operations research and computer science techniques in agriculture.”
The Syngenta challenge goal was to create a model for seed prediction to help farmers decide which seed variety to plant in upcoming seasons.
“Global food security is one of the greatest challenges facing the next generation, and there is a significant need to engage a broader talent base into agriculture,” Joseph Byrum, lead for the Syngenta Crop Challenge in Analytics committee, said. “This competition clearly demonstrated that people outside and adjacent to the industry can make noteworthy contributions.”
The Stanford team won $5,000 for its entry.
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