DOI: 10.5281/zenodo.14899968
title: Ornamental Flower Plants Dataset

description: Description:






The Ornamental Flower Plants Dataset is curated to assist in the development of accurate image classification models for various species of ornamental plants. Ideal for machine learning practitioners, researchers, and botanists, this dataset is structured to facilitate training and testing models that focus on plant recognition and classification. It contributes to enhancing biodiversity monitoring, plant taxonomy, and conservation through technological advancements.







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Dataset Features



Image Format: The dataset contains high-quality images in JPEG format, all resized to 224×224 pixels to ensure uniformity in model training.

Split Structure:



Training Set: Contains approximately 700 images of ornamental flowers, capturing various angles, lighting conditions, and environments to simulate real-world data variability.

Test Set: Comprises 150 images used for model evaluation, allowing for accurate performance measurement after training.



Categorization: Each image in the dataset is categorized by flower species, ensuring diversity in floral morphology, color, size, and regional varieties.


Context and Importance

In the modern era, flower identification has numerous applications ranging from educational tools to horticultural management. With increasing environmental concerns, this dataset serves as a valuable resource for automating plant recognition systems. Whether integrated into mobile applications for hobbyists or leveraged in large-scale agricultural systems, this dataset can assist in identifying and cataloging plant species effortlessly.

Data Enhancement Opportunities



Extended Variety: Expanding the dataset by adding more species of flowers, including those from rare or endangered categories, would greatly enhance the classification scope.

Additional Metadata: Adding extra information such as blooming seasons, geographic regions, and common uses (e.g., medicinal, decorative) would increase the dataset’s applicability across different fields like environmental science and agriculture.

Augmentation Techniques: To further improve model generalization, data augmentation (such as rotating, flipping, and varying brightness) could be applied to create synthetic variations of the original images.


Inspiration for Model Development

The inspiration behind this dataset is to push the boundaries of ornamental plant identification by building models that outperform existing flower classification tools. The goal is to develop an intuitive, easy-to-use system capable of classifying multiple flower species with high precision. Such systems can be useful in conservation efforts, eco-tourism, educational tools, or gardening aids.

Potential Applications



Mobile Applications: Use the dataset to develop apps that allow users to snap a picture and identify flowers instantly.

Agricultural Systems: Employ the dataset for AI-driven tools that assist farmers in monitoring ornamental plant health and identifying potential threats.

Conservation Efforts: Aid botanists and environmentalists in cataloging and preserving endangered flower species through automated systems.


Conclusion

The Ornamental Flower Plants dataset is an excellent starting point for developing sophisticated image classification models tailored to plant recognition. Its potential to be expanded and its diverse applications across industries make it an invaluable resource for AI practitioners and researchers working on environmental and botanical projects.

This dataset is sourced from Kaggle.