RoseLeafInsight: A High-Resolution Image Dataset for Rose Leaf Disease Recognition
Description
RoseLeafInsight is a meticulously curated high-resolution image dataset designed for the classification and recognition of various rose leaf conditions using machine learning and computer vision techniques. This dataset includes four categories of rose leaves: Healthy, Black Spot, Insect Hole, and Yellow Mosaic Virus, providing a diverse set of images for disease detection and automated plant health monitoring. Each category is well-represented, ensuring a balanced dataset suitable for developing deep learning models for classification, segmentation, and disease detection tasks. Dataset Composition: The dataset consists of a total of 3,228 high-resolution images, distributed across the following categories: 1. Healthy Leaf: 1,686 images 2. Black Spot: 409 images 3. Insect Hole: 453 images 4. Yellow Mosaic Virus: 680 images Geographical Location of Data Collection: The rose leaf images were collected from two distinct locations in Bangladesh, ensuring diversity in environmental conditions and plant health variations: 1. Zailla, Singair, Manikganj - Latitude: 23°47'46.11"N - Longitude: 90°13'15.73"E 2. Golap Gram, Sadullapur-Komolapur, Road Birulia Bridge, Dhaka 1216 - Latitude: 23°50'6.108''N - Longitude: 90°18'31.5108''E These locations are known for their extensive rose cultivation, making them ideal for collecting a dataset that captures real-world variations in rose leaf health and disease conditions. Preprocessing Details: To enhance model performance and standardize input images, the following preprocessing steps were applied: • Resizing: All images were resized to 3000 × 3000 pixels for uniformity. • Background Removal: Unwanted backgrounds were eliminated to focus on leaf features. • Brightness Enhancement: The brightness of each image was adjusted by a factor of 1.2 to improve visibility and contrast. Potential Applications: RoseLeafInsight is ideal for training and evaluating machine learning and deep learning models in various applications, including: • Automated plant disease detection systems • Smart agriculture and precision farming • Image-based disease diagnosis for plant pathology research • Transfer learning and fine-tuning deep learning models for plant health classification This dataset provides a valuable resource for researchers, agronomists, and AI practitioners seeking to develop robust solutions for real-time rose leaf disease detection.