In this demonstrator, we applied innovative technology to detect and identify pollinators in the field and automatize the detection and quantification of pollinators in addition to classical measurements. Furthermore, we developed and tested a new maize-based cropping system that includes phacelia or a commercial flowering strip mixture in a maize field (intercropping) to allow for a large area of connected fodder supply to pollinators while aiming for minimized maize yield losses due to competition.
The video shows the field experiment and the observations in the year 2023. First results show that the maize-based intercropping system significantly increased insect abundance compared to sole maize and could potentially connect isolated areas while providing maize yield. Our machine learning approach to non-invasively and automatically detect and count pollinators in RGB images has a high potential to evaluate cropping systems (e.g., flower strip seeds) or management impacts (e.g., weeding) on insect abundance.