Wild Berry image dataset collected in Finnish forests and peatlands using drones
This dataset addresses the challenge of automating berry detection for sustainable harvesting in Finland, but it is incremental as it focuses on new data rather than novel methods.
The authors introduced WildBe, the first drone-captured image dataset of wild berries in Finnish forests and peatlands, featuring 3,516 images with 18,468 annotated bounding boxes, and evaluated six object detectors for berry detection across varied conditions.
Berry picking has long-standing traditions in Finland, yet it is challenging and can potentially be dangerous. The integration of drones equipped with advanced imaging techniques represents a transformative leap forward, optimising harvests and promising sustainable practices. We propose WildBe, the first image dataset of wild berries captured in peatlands and under the canopy of Finnish forests using drones. Unlike previous and related datasets, WildBe includes new varieties of berries, such as bilberries, cloudberries, lingonberries, and crowberries, captured under severe light variations and in cluttered environments. WildBe features 3,516 images, including a total of 18,468 annotated bounding boxes. We carry out a comprehensive analysis of WildBe using six popular object detectors, assessing their effectiveness in berry detection across different forest regions and camera types. WildBe is publicly available on HuggingFace at https://huggingface.co/datasets/FBK-TeV/WildBe.