2021 Winners

Congratulations to the winners of the Syngenta 2021 Crop Challenge

First Place
Reena Kapoor and Rodolfo García-Flores

Second Place
Mingshi Cui, Kunting Qi, and Byran Smucker

Third Place
Saiara Samira Sajid and Guiping Hu


2021 Finalists

The 2021 Syngenta Crop Challenge finalists are:
Team Members Submission Title
Reena Kapoor and Rodolfo García-Flores affiliated with CSIRO Data61 (Australia) Optimal Schedules for Corn Planting and Storage
Javad Ansarifar, Faezeh Akhavizadegan and Lizhi Wang from Iowa State University (USA) Scheduling Planting Time Through Developing an Optimization Model and Analysis of Time Series Growing Degree Units
Saiara Samira Sajid and Guiping Hu from Iowa State University (USA) Optimizing Crop Planting Schedule Considering Planting Window & Harvesting Capacity
Mingshi Cui, Kunting Qi, and Byran Smucker from Miami University (USA) A Multiobjective, Soft Constraint Solution to the 2021 Syngenta Crop Challenge

2020 Winners

Congratulations to the winners of the Syngenta 2020 Crop Challenge

First Place
Javad Ansarifar, Faezeh Akhavizadegan and Lizhi Wang

Second Place
Shouyi Wang, Jie Han, Fangyun Bai and Ho Manh Lin

Third Place
Craig A. Rolling, Isaac Akogwu, Christopher Cotter and Yalda Zare


2020 Finalists

The 2020 Syngenta Crop Challenge finalists are:
Team Members Submission Title
Javad Ansarifar, Faezeh Akhavizadegan and Lizhi Wang from Iowa State University (USA) Yield Performance of Plant Breeding Prediction with Interaction Based Algorithm
Shouyi Wang, Jie Han, Fangyun Bai and Ho Manh Linh from University of Texas at Arlington (USA) Hybrid Crop Yield Prediction Using Deep Factorization Methods with Integrated Modeling of Implicit and Explicit High-Order Latent Variable
Craig A. Rolling, Isaac Akogwu, Christopher Cotter and Yalda Zare from Benson Hill (USA) Combining Strong Learners to Predict Yield of Maize Hybrids
Saeed Khaki, Zahra Khalilzadeh and Lizhi Wang from Iowa State University (USA) Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach
Pythagoras Karampiperis, Sotiris Konstantinidis, Antonis Koukourikos and Panagiotis Zervas from SCiO (Greece) H4H: A Hybrid Approach Combining Descriptive Statistics and Collaborative Filtering for Predicting the Performance of Hybrid Breeding

2019 Winners

Congratulations to the winners of the Syngenta 2019 Crop Challenge

First Place
Bogdan Georgiev, Kostadin Cvejoski, Cesar Ojeda, Jannis Schuecker and Anne-Katrin Mahlein

First Place Winners

Second Place
Saeed Khaki and Zahra Khalilzadeh

Second Place Winners

Third Place

Gordan Mimic, Sanja Brdar, Milica Brkic, Marko Panic, Oskar Marko and Vladimir Crnojevic

Third Place Winners


2019 Finalists

The 2019 Syngenta Crop Challenge finalists are:
Team Members Submission Title
Gordan Mimic, Sanja Brdar, Milica Brkic, Marko Panic, Oskar Marko and Vladimir Crnojevic from the BioSense Institute, Serbia Engineering meteorological features to select stress tolerant hybrids in maize
Bogdan Georgiev, Kostadin Cvejoski, Cesar Ojeda, Jannis Schuecker and Anne-Katrin Mahlein from the Fraunhofer Center for Machine Learning, Fraunhofer IAIS, Germany Combining expert knowledge and neural networks to model environmental stresses in agriculture
Konstantin Divilov from Oregon State University, USA An ecophysiological Bayesian approach to identify heat and drought tolerant maize hybrids
Saeed Khaki and Zahra Khalilzadeh from Iowa State University, USA Crop stress classification using deep convolutional neural networks

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

The Winner's Story

Nicolas Martin, Hugo Dorado Bentencourt, Andres Aguilar, Daniel Jimenez, Sylvain Delerce, Dan Dyer

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

Joseph Byrum, Marko Panić, Oskar Marko, Robin Lougee, Stefan Karisch

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

Second place winners

Third Place

Durai Sundaramoorthi, Lingxiu Dong, Iva Rashkova, Piruthiviraj Sivaraj

Third place winners

"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
Wenjun Zhou
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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