Wild Berry image dataset collected in Finnish forests and peatlands using drones

TL;DR

This paper introduces WildBe, a novel drone-captured image dataset of Finnish wild berries in natural habitats, enabling improved detection and analysis of berries in challenging forest environments.

Contribution

WildBe is the first dataset of its kind, featuring diverse berry types and environmental conditions, facilitating advancements in automated berry detection in natural settings.

Findings

Effective detection of berries using six object detectors

Dataset covers severe light variations and cluttered environments

Provides a valuable resource for future research in forest berry analysis

Abstract

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.