We monitor insects ensuring sustainable crop utilisation

Copenhagen, Denmark

Agricultural & Food Technologies


At FaunaPhotonics, we aim to quantify nature to ensure sustainable crop utilization. Our sensors offer the ability to do in-field real-time continuous monitoring of insect populations and is at the frontier of developments within hyperlocal ground level monitoring stations. Insects cause massive negative impact in agriculture. Every year agricultural insect pests damage 15% of crops equating to annual losses of about €18 billion in the USA alone opening a market for monitoring agricultural insect pests of more than €1 billion annually. We offer users the opportunity to make better-informed decisions by getting access to more frequent data and developments in insect pest populations over time. This will allow farmers to better time pesticide application. Large multinationals are moving from selling machinery, seed and chemicals, to develop matrix-crunching software platforms that will act as farm-management systems. To make this work, sensor providers such as FaunaPhotonics are needed to improve data collection procedures to allow for more sustainable agriculture. Data science will tie together the information and this data will maximize the use of resources to produce the most food for the least amount of inputs. Accurate knowledge about the presence of pest insects in agriculture is a key component in developing new and more sustainable treatment strategies. The more data we collect the more exponentially valuable the data becomes. We are a team with background in photonics, LIDAR, nano-optics, electrical engineering and business. FaunaPhotonics fast facts: we have developed lab models, field prototypes and performed field testing. We won the Agro Business Park Innovation Competition in 2014, Green Tech Challenge in 2015 (IBM Cloud Credit Award worth $120.000) and were award winners at the European Venture Contest in 2015 – Top 5 CleanTech ventures. FaunaPhotonics was part of top 52 finalists out of 380 in the CODE_N Contest 2016 in Karlsruhe, Germany.