Tropical Flower Dataset: Seven Species from Bangladesh for Classification and Ecological Research

Description: This dataset presents a collection of carefully annotated images of seven commonly found tropical flower species, aimed at advancing the capabilities of machine learning and computer vision models in flower detection, classification, and recognition. Collected with the intent to capture diverse environmental contexts, this dataset offers a unique opportunity for researchers and practitioners in botany, agriculture, ecology, and AI to study tropical flowers in various. Dataset Content: This dataset leverages a comprehensive dataset comprising 4,319 images of seven tropical flower species, with variability in backgrounds, lighting conditions, and growth stages to provide comprehensive data diversity meticulously curated to support machine learning applications in automated species identification and ecological monitoring. The dataset captures diverse natural settings and various stages of flower development, ensuring a robust foundation for image-based classification and detection tasks. 1. Rose: 827 images 2. Bougainvillea: 580 images 3. Marigold: 717 images 4. Hibiscus: 548 images 5. Crown of Thorns: 583 images 6. Jungle Geranium: 698 images 7. Madagascar Periwinkle: 366 images Purpose: The purpose of this dataset is to help create machine learning models that accurately recognize and classify tropical flowers, aiding in biodiversity studies and education about plant species.