FlowerMaskDataset
Description
In recent years, image-based analysis using machine learning and deep learning has become essential in agriculture, botany, and environmental studies. However, one of the major challenges is the lack of high-quality, well-annotated flower datasets suitable for advanced computer vision tasks like semantic segmentation. Most existing flower datasets lack pixel-level annotations or were captured in controlled lab environments. They often fail to represent real-world conditions such as varying lighting, complex natural backgrounds, and different flower orientations. FlowerMaskDataset was created to fill this gap by providing a segmentation-ready dataset collected under practical, real-life scenarios. Dataset Overview FlowerMaskDataset contains real-world images of 6 different flower species: Butterfly Pea, Caesalpinia Pulcherrima, Rose, Plumeria, Tecoma Stans, and Jatropha Integerrima. Images were collected under natural conditions with variations in lighting, angles, backgrounds, and positions. Key Statistics Total images collected: 3,600 (600 images per flower species) Annotated images: 3,600 Annotation tool: LabelMe (polygon-based manual annotation) Files per annotated image: Original RGB image, Segmentation Mask, Foreground image, Background image, and JSON annotation file Features ~High-quality manual polygon annotations for accurate foreground-background separation ~Suitable for semantic segmentation and object detection tasks ~Includes documentation on the botanical, medicinal, ecological, biological, and practical significance (including potential biofuel uses) of each flower species Purpose This dataset serves as a strong foundation for training and evaluating machine learning and deep learning models in: Flower detection Semantic image segmentation Plant classification and recognition Precision agriculture and environmental monitoring FlowerMaskDataset bridges technology and nature by combining high-quality annotated images with valuable botanical insights. Keywords Flower Detection, Image Segmentation, Semantic Segmentation, Flower Dataset, Plant Recognition, Machine Learning, Deep Learning, LabelMe, Polygon Annotation, Butterfly Pea, Caesalpinia Pulcherrima, Rose, Plumeria, Tecoma Stans, Jatropha Integerrima Acknowledgement to VIT Bhopal Students: Devansh Bansal, Venya Rajput, Rishi Raj, Sourima Dutta, Shrestha Agarwal, Priyanshi Prajapati Note: For complete Dataset kindly email to: seedtosuccess2025@gmail.com
Files
Institutions
- VIT Bhopal UniversityMadhya Pradesh, Kothri Kalān