Herbarium Image Segmentation Dataset with Plant Masks for Enhanced Morphological Trait Analysis

The Herbarium Image Segmentation Dataset originates from the MNHN (Muséum National d’Histoire Naturelle) in Paris and includes 11 diverse plant families and genera, offering a rich variety within dicotyledons. The dataset comprises 2,277 RGB images, each paired with a corresponding segmentation mask. These images cover a range of genera: Amborella (91 images), Castanea (161), Desmodium (164), Ulmus (352), Rubus (184), Litsea (199), Eugenia (219), Laurus (250), Convolvulaceae (177), Magnolia (162), and Monimiaceae (318), showcasing significant morphological diversity.This dataset was generated by removing non-plant backgrounds to enhance the clarity of plant features. It is suitable for segmentation tasks in botanical research and supports studies on plant morphology, biodiversity, and conservation. The segmented images can improve accuracy in classification tasks, particularly in identifying plant morphological traits, and are intended to facilitate research in plant science, biodiversity, and conservation.