Abstract
Recently, object recognition and image segmentation have gained much attention in the computer vision field and image processing for effective object localisation and identification. Researchers have applied semantic segmentation and instance segmentation in diverse application areas. However, the least research has been performed in natural habitat monitoring or plant species identification in natural environments/surroundings. For this study, we composed a real image dataset from four habitats: forests, dunes, grasslands, and screes from various locations in Italy. Habitat expert botanists annotated the data using bounding box annotations which have been further utilised to generate the plant species masks using the recently proposed Segment Anything Model (SAM) for segmentation, localisation, and identification tasks. Extensive experimentation has been performed on habitat data with bounding boxes and masks using YOLOv8 detection and segmentation models. Comparative analysis of models, model training with different train data percentages, and the importance of masks over bounding boxes have been studied and discussed.
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Acknowledgement
This research was supported by Grant agreement No. 101016970, European Union’s Horizon 2020 Research and Innovation Programme - ICT-47-2020.
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Kaur, P. et al. (2023). Segmentation and Identification of Mediterranean Plant Species. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14362. Springer, Cham. https://doi.org/10.1007/978-3-031-47966-3_34
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