Wageningen Summer School: Advancing Skills in Plant Phenotyping

Our youngest team members, Marko Mladenović and Aleksandar Kovačević, have successfully completed the Summer School on Image Analysis in Plant Phenotyping, organized by Wageningen Academy at WUR University in Wageningen, Netherlands, from June 24th to June 28th. Each day was structured into theoretical lessons and practical tasks.

The first day focused on the creation and filtering of images for plant phenotyping. On the second day, the topics included the use of drones and other devices for aerial plant phenotyping, image segmentation, and the potential applications of aerial phenotyping in the industry. Additionally, coursework on the second day encompassed comprehensive image analysis and orthomosaic creation, calculation of plant height per plot, and various vegetation indices, which will be particularly useful in future work. Participants also toured the university’s entire infrastructure used for high-throughput phenotyping, including NPEC greenhouses and plant growth chambers, various drones and aerial imaging devices, and the TraitSeeker machine.

The third and fourth days of the summer school were dedicated to machine learning and deep learning, used for predicting desired traits. On the final day of the summer school, the topic of experimental design for plant phenotyping was covered, followed by image processing using images provided by the participants. The summer school concluded with a final test, which was successfully passed by our team members, earning them certificates.

The knowledge gained from this summer school confirmed that the field of aerial plant phenotyping is on the rise and represents the future, both in plant breeding and plant production. Wageningen University & Research was reaffirmed as one of the world’s leading universities in agricultural research. The university campus is excellently organized, and the university possesses state-of-the-art equipment for plant phenotyping. Participation in this summer school has provided knowledge that will be valuable for the PHENO_MaizE project.