2018 Winners

Congratulations to the winners of the Syngenta 2018 Crop Challenge

First Place
Andres Aguilar, Hugo “Andres” Dorado Bentencourt, Sylvain Delerce,
Michael Caraccio, Juan Camilo Rivera, Maria Camila Gomez,
Steven Humberto Sotelo and Anestis Gkanogiannis

Left to right: Crop Challenge committee chair Nicolas Martin;
CIAT team members Hugo “Andres” Dorado Bentencourt, Andres Aguilar,
Daniel Jimenez and Sylvain Delerce; and Syngenta head of seed product development Dan Dyer.


What the Winners Had to Say

Second Place
Jhonathan Pedroso Rigal dos Santos

Third Place

Saeed Khaki, Hans Mueller and Lizhi Wang



2018 Finalists

The 2018 Syngenta Crop Challenge finalists are:
Team Members Submission Title
Jacques Ehret and Patrick Vetter, Supper & Supper GmbH, Berlin, Germany A simple way to predict crop yields, using Multiple Factor Analysis, Random Forests and spatio-temporal weather monthly forecast

Jhonathan Pedroso Rigal dos Santos, Sao Paulo, Brazil Bridging concepts from Bayesian theory, Artificial Intelligence and Genetics: A novel Bayesian Network methodology for predictions and decision-making

Saeed Khaki, Hans Mueller and Lizhi Wang, Iowa State University, Ames, US Genotype - Environment Interaction (G by E) Analysis using Deep Neural Networks Approach

Andres Aguilar, Sylvain Delerce, Hugo “Andres” Dorado Bentencourt, Michael Caraccio, Juan Camilo Rivera, Maria Camila Gomez, Steven Humberto Sotelo and Anestis Gkanogiannis, CIAT, Palmira, Colombia Speeding up maize hybrids breeding schemes using machine learning

Rodrigo Gonçalves Trevisan, Jackeline Pedriana Borba and Júlia Silva Morosini, Piracicaba, Brazil Using Deep Learning to predict maize performance


2017 Winners

Congratulations to the winners of the Syngenta 2017 Crop Challenge

First Place
Oskar Marko, Sanja Brdar, Marko Panić, Isidora Šašić, Milivoje Knežević,
Danica Despotović, Vladimir Crnojević

From Left: Joseph Byrum (Syngenta), Winners Marko Panić and Oskar Marko,
Crop Challenge committee chair Robin Lougee, INFORMS representative Stefan Karisch.

Second Place
Zhongshun Shi, Yu Zhao, Xi Zhang, Leyuan Shi

Third Place

Durai Sundaramoorthi, Lingxiu Dong, Iva Rashkova, Piruthiviraj Sivaraj

"Our four-layer approach shows how similarly grown varieties can yield differently."
– Durai Sundaramoorthi, Washington University in St. Louis


What the Winners Had to Say


2017 Finalists

Finalists for the Syngenta 2017 Crop Challenge are:

Team Members Submission Title
Oskar Marko, Sanja Brdar, Marko Panić, Isidora Šašić, Milivoje Knežević, Danica Despotović, Vladimir Crnojević Portfolio Optimisation for Seed Selection in Diverse Weather Scenarios

Zhongshun Shi, Yu Zhao, Xi Zhang, Leyuan Shi A Decision Making Approach for Soy Seed Variety Selection via Hedging Against Weather Risk

Benjamin Harlander, Taylor Thiel Soybean Portfolio Selection with LASSO Model Averaging and Integer Linear Programming

Durai Sundaramoorthi, Lingxiu Dong, Iva Rashkova, Piruthiviraj Sivaraj A Hierarchical-Ensemble of Machineries to Optimize the Choice of Soybean Varieties

Yunhe Feng, Wenjun Zhou Seed Stocking Via Multi-Task Learning

2017 Finalist Wenjun Zhou, University of Tennessee



2016 Winners

Congratulations to the winners of the Syngenta 2016 Crop Challenge

First Place
Xiaocheng Li
Huaiyang Zhong
Associate Professor David Lobell
Associate Professor Stefano Ermon

Second Place
Nataraju Vusirikala
Mehul Bansal
Prathap Siva Kishore Kommi

Third Place
Bhupesh Shetty
Ling Tong
Samuel Burer

What the Winners Had to Say



2016 Finalists

Finalists for the Syngenta 2016 Crop Challenge are:

Team Members Submission Title
Bhupesh Shetty, Ling Tong, Samuel Burer Balancing weather risk and crop yield for soybean variety selection

Yu Zhao, Jingsi Huang, Ming Qin Soy variety selection to maximize yield and minimize risk based on neural network prediction and portfolio theory

Mark Rees, Yidong Peng, Jaremy Babila, Mike Lyons, Lily Huang, Yinghan Song, Chun-Yang Wei, Susan Arnot The selection of the best soybean varieties for hedging risk of weather uncertainties-a deep learning and heuristic optimization approach

Oskar Marko, Sanja Brdar, Marko Panic, Predrag Lugonja Soybean varieties portfolio optimisation based on yield prediction using weighted histograms

Nataraju Vusirikala, Mehul Bansal, Prathap Siva Kishore Kommi Decision assist tool for seed variety selection to provide best yield in known soil and uncertain future weather conditions

Xiaocheng Li, Huaiyang Zhong, David Lobell, Stefano Ermon Hierarchy modeling of soybean variety yield and decision making for future planting plan


This contest is being administered by The Analytics Society of INFORMS.

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