Syngenta and the Analytics Society of INFORMS have announced the finalists for the 2018 Syngenta Crop Challenge in Analytics.
Now in its third year, the competition—which is hosted by the Analytics Society of the Institute for Operations Research and the Management Sciences (INFORMS), aims to address the challenge of achieving global food security by fostering cross-industry collaboration between agriculture and advanced analytics experts.
Challenge participants were given a real-world dataset and asked to develop models that predict how well corn hybrids will perform in untested locations. These predictive models can help plant breeders decide which hybrids to advance and ultimately offer to growers.
“In selecting the finalists for this competition, we looked at the rigor, clarity and innovation of the submissions we received,” says Nicolas Martin, assistant professor at the University of Illinois at Urbana-Champaign, Crop Challenge prize committee chair and member of INFORMS. “This distinguished class of finalists submitted models that are both creative and sophisticated. We recognize and appreciate the time and thought that went into crafting the entries.”
The 2018 Syngenta Crop Challenge finalists, as selected by an INFORMS panel of experts representing diverse technical backgrounds, are:
· A simple way to predict crop yields, using Multiple Factor Analysis, Random Forests and spatio-temporal weather monthly forecast РJacques Ehret and Patrick Vetter, Supper & Supper GmbH, Berlin, Germany
¬∑ Bridging concepts from Bayesian theory, Artificial Intelligence and Genetics: A novel Bayesian Network methodology for predictions and decision-making – Jhonathan Pedroso Rigal dos Santos, São Paulo, Brazil
· Genotype РEnvironment Interaction (G by E) Analysis using Deep Neural Networks Approach РSaeed Khaki, Hans Mueller and Lizhi Wang, Iowa State University, Ames, Iowa, U.S.
¬∑ Speeding up maize hybrid breeding schemes using machine learning –
Andres Aguilar, Sylvain Delerce, Michael Caraccio Lausanne, Juan Camilo Rivera, Maria Camila Gomez, Steven Humberto Sotelo and Anestis Gkanogiannis, CIAT, Palmira, Colombia
¬∑ Using Deep Learning to predict maize performance – Rodrigo Gonçalves Trevisan, Jackeline Pedriana Borba and Júlia Silva Morosini, Piracicaba, Brazil
The finalists have been invited to present their submissions during the 2018 INFORMS Conference on Business Analytics & Operations Research in Baltimore, Maryland. They will be evaluated on the quality and clarity of their presentations, and the results will be announced on April 17. The winner will be awarded $5,000; the runner-up will receive $2,500; and the third place entry will receive $1,000.
“In order to meet the needs of a growing world population, the agriculture industry will need to use a variety of tools successfully and look to several disciplines for innovation,” says Gregory Doonan, Crop Challenge judge and head of novel algorithm advancement, Syngenta. “That is why I am so excited about the Syngenta Crop Challenge. Our partnership with INFORMS allows us to reach data analytics experts to raise awareness and help us confront the challenge of global food security.”
The Syngenta Crop Challenge in Analytics was established in 2015 with funding provided by prize winnings awarded to Syngenta in connection with its receipt of the 2015 Franz Edelman Award for Achievement in Operations Research and the Management Sciences. The challenge aligns with Syngenta’s commitment to make crops more efficient, one of the tenets of The Good Growth Plan, a global initiative to improve the sustainability of agriculture.
For more information about the Syngenta Crop Challenge in Analytics, visit www.ideaconnection.com/syngenta-crop-challenge.
