The following samples show some promising results of an automated weed instance detector that works in cereal fields and is able to distinguish monocot (red) from dicot (blue) weeds. It utilizes the focal-loss introduced by Facebook AI Research (FAIR), which balance the error contribution based on how easy detectable they are.
Even though the final aim is to classify each weed instance, it is sometimes not possible due to the small size of the weeds. In this case a mono/dicot discrimination is still valuable to determine a suitable weed control strategy.
For more information, contact Mads Dyrmann
“Man starter med at skaffe sig overblik over hvor ukrudttet er ude i ens kornmark, og så sprøjter man bagefter, men kun de steder, der er ukrudt. Sådan lyder den besnærrende enkle opskrift, som et hold forskere fra Aarhus Universitet lige nu arbejder på i samarbejder med flere teknologivirksomheder.”
A drone video from a day where we collected ~16,000 images/140ha in winter cereals from Ørtoft in Northern Jutland:
Inspired by yesterday’s visit to Agritechnica and the booths by Bayer/Xarvio/Bosch demonstrating precision weed localization and spraying, I decided to make a small video of the our current weed-decrimination capabilities:
/ Mads Dyrmann
The plant seedlings dataset, made in collaboration with University of Southern Denmark and Aarhus University in Flakkebjerg, has how been moved to this site.
The dataset contains images of approximately 960 unique plants belonging to 12 species at several growth stages. Take a look at it here.