Winners of the 2019 Syngenta Crop Challenge in Analytics Announced
The fourth annual competition showcased analytical models to assess a corn plant’s ability to handle heat and drought.
This spring, winners of the fourth annual Syngenta Crop Challenge in Analytics celebrated their victory during the INFORMS Conference on Business Analytics & Operations Research. Since 2015, the challenge has fostered cross-industry collaboration between agriculture and data analytics to help address growing global food demands. Participants in this year’s competition were asked to develop models to assess corn hybrids’ ability to handle heat and drought stresses.
The 2019 winning team represents the Fraunhofer Research Center for Machine Learning in Germany, one of Europe’s leading research institutions for applied big data and artificial intelligence.
In addition to Cvejoski, the five-member team includes Bogdan Georgiev, César Ojeda, Jannis Schuecker and Anne-Katrin Mahlein. Their submission, “Combining Expert Knowledge and Neural Networks to Model Environmental Stresses in Agriculture,” earned them a $5,000 prize.
Saeed Khaki and Zahra Khalilzadeh from Iowa State University secured second place and received a $2,500 prize for their submission, “Crop Stress Classification Using Deep Convolutional Neural Networks.”
A team from the BioSense Institute in Serbia, whose members include Gordan Mimić, Sanja Brdar, Milica Brkić, Marko Panić, Oskar Marko and Vladimir Crnojević, won third place and received a $1,000 prize for their submission, “Engineering Meteorological Features to Select Stress-Tolerant Hybrids in Maize.”
The 2020 Syngenta Crop Challenge in Analytics will launch later this year.
The 2019 winning team represents the Fraunhofer Research Center for Machine Learning in Germany, one of Europe’s leading research institutions for applied big data and artificial intelligence.
Winners in the 2019 @Syngenta Crop Challenge in #Analytics are announced.
“Our institute has recently become interested in combining machine learning with agriculture,” says team member Kostadin Cvejoski. “In the future, we are going to work more on solving agricultural problems.”
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In addition to Cvejoski, the five-member team includes Bogdan Georgiev, César Ojeda, Jannis Schuecker and Anne-Katrin Mahlein. Their submission, “Combining Expert Knowledge and Neural Networks to Model Environmental Stresses in Agriculture,” earned them a $5,000 prize.
“Cross-discipline collaboration can help us discover new ways to use agriculture data to inform seed breeding research and development,” says Gregory Doonan, head of novel algorithm advancement at Syngenta and a 2019 Crop Challenge judge. “The winning team represented the forward-thinking approach needed to improve crop productivity to meet the needs of a growing population.”“Cross-discipline collaboration can help us discover new ways to use agriculture data to inform seed breeding research and development.”
Saeed Khaki and Zahra Khalilzadeh from Iowa State University secured second place and received a $2,500 prize for their submission, “Crop Stress Classification Using Deep Convolutional Neural Networks.”
A team from the BioSense Institute in Serbia, whose members include Gordan Mimić, Sanja Brdar, Milica Brkić, Marko Panić, Oskar Marko and Vladimir Crnojević, won third place and received a $1,000 prize for their submission, “Engineering Meteorological Features to Select Stress-Tolerant Hybrids in Maize.”
The 2020 Syngenta Crop Challenge in Analytics will launch later this year.
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