Winners of the 2019 Syngenta Crop Challenge in Analytics Announced
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.
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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.