Syngenta and the Analytics Society of INFORMS announce finalists for 2020 Syngenta Crop Challenge in Analytics
−− Competition received novel submissions from data analytics, mathematics and statistics students and professionals worldwide
Apr. 1, 2020
Now in its fifth year, the Syngenta Crop Challenge in Analytics is a collaborative effort between Syngenta and the Analytics Society of the Institute for Operations Research and the Management Sciences (INFORMS). The competition brings together experts in data analytics, mathematics and statistics, underscoring the importance of cross-industry collaboration in addressing challenges unique to agriculture.
The 2020 Syngenta Crop Challenge finalists, as selected by the prize committee and listed in no particular order, are:
- Yield Performance of Plant Breeding Prediction with Interaction Based Algorithm – Javad Ansarifar, Faezeh Akhavizadegan and Lizhi Wang from Iowa State University (USA)
- Hybrid Crop Yield Prediction Using Deep Factorization Methods with Integrated Modeling of Implicit and Explicit High-Order Latent Variable Interactions – Shouyi Wang, Jie Han, Fangyun Bai and Ho Manh Linh from University of Texas at Arlington (USA)
- Combining Strong Learners to Predict Yield of Maize Hybrids – Craig A. Rolling, Isaac Akogwu, Christopher Cotter and Yalda Zare from Benson Hill (USA)
- Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach – Saeed Khaki, Zahra Khalilzadeh and Lizhi Wang from Iowa State University (USA)
- H4H: A Hybrid Approach Combining Descriptive Statistics and Collaborative Filtering for Predicting the Performance of Hybrid Breeding – Pythagoras Karampiperis, Sotiris Konstantinidis, Antonis Koukourikos and Panagiotis Zervas from National Center for Scientific Research Demokritos (Greece)
The 2020 Syngenta Crop Challenge tasked participants to deliver analytical approaches to improve complex crop breeding processes. Using real-world crop data, entrants developed models predicting the performance of various genetic combinations in corn.
“The prize committee was unanimous in its praise for the creativity and sophistication of the models submitted by this year’s finalists,” said Durai Sundaramoorthi, area coordinator and senior lecturer of data analytics at Olin Business School, Washington University in St. Louis, and Crop Challenge prize committee chair. “The complexity in agriculture is vast, and these finalists’ submissions demonstrated how solutions rooted in data analytics and machine learning can help the industry navigate the multi-variable nature of plant breeding.”
The finalists have been invited to present their submissions remotely to the prize committee in April 2020. The first-place winner will receive $5,000; the second-place winner will receive $2,500; and third place will receive $1,000.
“This competition has once again brought to light how agriculture – and plant breeding, specifically – can benefit from ideas contributed by some of the brightest minds in data analytics, mathematics, statistics and machine learning,” said Gregory Doonan, head of advanced analytics, Syngenta, and Crop Challenge judge. “This segment of the industry has seen great advancements in recent years, and data analytics has played a driving role.”
Established in 2015, the Syngenta Crop Challenge in Analytics is supported by Syngenta and hosted by the Analytics Society of INFORMS. It was initially funded by prize winnings donated by Syngenta in connection with the company’s 2015 win of the Franz Edelman Award for Achievement in Operations Research and the Management Sciences, an international award that recognizes excellence in the industry.
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