Wild Berry image dataset collected in Finnish forests and peatlands using drones
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arXiv
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| Natura: | Preprint |
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2024
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| author | Riz, Luigi Povoli, Sergio Caraffa, Andrea Boscaini, Davide Mekhalfi, Mohamed Lamine Chippendale, Paul Turtiainen, Marjut Partanen, Birgitta Ballester, Laura Smith Noguera, Francisco Blanes Franchi, Alessio Castelli, Elisa Piccinini, Giacomo Marchesotti, Luca Couceiro, Micael Santos Poiesi, Fabio |
| author_facet | Riz, Luigi Povoli, Sergio Caraffa, Andrea Boscaini, Davide Mekhalfi, Mohamed Lamine Chippendale, Paul Turtiainen, Marjut Partanen, Birgitta Ballester, Laura Smith Noguera, Francisco Blanes Franchi, Alessio Castelli, Elisa Piccinini, Giacomo Marchesotti, Luca Couceiro, Micael Santos Poiesi, Fabio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07550 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Wild Berry image dataset collected in Finnish forests and peatlands using drones Riz, Luigi Povoli, Sergio Caraffa, Andrea Boscaini, Davide Mekhalfi, Mohamed Lamine Chippendale, Paul Turtiainen, Marjut Partanen, Birgitta Ballester, Laura Smith Noguera, Francisco Blanes Franchi, Alessio Castelli, Elisa Piccinini, Giacomo Marchesotti, Luca Couceiro, Micael Santos Poiesi, Fabio Computer Vision and Pattern Recognition 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. |
| title | Wild Berry image dataset collected in Finnish forests and peatlands using drones |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.07550 |