"In my career we get fed up with purely academic papers and research, and challenges are the thing to do to take on the real problems and see how it goes. And if you want to show yourself where you are and where you stand, you’ve got to go for it."
- Professor Vladimir Crnojević, first place team
"After receiving recognition as runners-up in the 2016 Syngenta Crop Challenge, there was a European call for projects to compete for the EU’s Horizon Award, and we applied for the agriculture grants. Out of 200 submissions we came first – and we received € 28 million in funding for our research institute in Serbia over the next seven years. For building a structure, employee payment, recruiting new people."
– Oskar Marko, first place team
The first Syngenta Crop Challenge in Analytics tasked teams to create a seed prediction method for farmers in the first case competition of its kind. Here are a few of the winners' responses as they reflected on the inaugural Challenge.
"It has been a wonderful experience working with Syngenta on this project. We are excited about the impact our project can have on improving crop yields and addressing food security challenges. We see lots of potential for modern operations research and computer science techniques in agriculture."
– Xiaocheng Li, first place team
"Our team is constantly looking for new innovative ways to help solve sustainability challenges using methods from computer science and operations research. The Syngenta Crop Challenge was a perfect opportunity to test our techniques on a challenging real world problem."
- Xiaocheng Li, first place team
"Definitely. Agriculture is vitally crucial to a nation’s economy and its people’s livelihood. Applying operations research techniques to the agricultural domain is a new kind of application area for operations research with great importance."
– Yu Zhao, first place team
"Agricultural innovation is far behind other fields of industry. The reasons may be the lack of data, and people's understanding about agriculture. More attention should be put on agriculture research especially with the most advanced OR techniques."
– Yu Zhao, first place team
We have heard a lot about data collection (e.g., via machines and satellites), but we haven't heard as much about prediction and optimization. So we thought the existing innovation was more on the "hardware" side than the "software" side.
– Prof. Sam Burer, third place team
An interdisciplinary approach is needed. We can combine the latest machine learning techniques with economic analytical framework to solve problems in agriculture.
– Yu Zhao, first place team
We now have a much broader view of agriculture – a very intricate system with many layers of people, technology, and nature that must perform well to serve the world's food needs.
– Prof. Sam Burer, third place team
It has been a wonderful experience working with Syngenta on this project. We are excited about the impact our project can have on improving crop yields and addressing food security challenges. We see lots of potential for modern operations research and computer science techniques in agriculture.
– Xiaocheng Li, first place team
Not many companies are willing to engage the community with their problems and data, and those that are willing to engage do so only for doing prediction. Syngenta has been willing to engage in both prediction and optimization/decision making, which is very valuable to students in analytics.
– Prof. Sam Burer, third place team