Rose Leaf Disease Detection Dataset

Roses are cherished worldwide as symbols of love, purity, and affection, and they also serve as an important commercial crop. However, various leaf diseases significantly impact their aesthetic value and economic profitability. To address this challenge, we developed a specialized dataset of rose leaf images, aimed at advancing research in machine learning for disease identification.

This dataset was collected through field visits to Golaap Gram, Allah Vorsha Nursery, and Daffodil Nursery. All images were captured under natural daylight to ensure clear and detailed visuals. The dataset is organized into four distinct categories, with both original and augmented versions:
Without Augmentation:
● Black Spot: 432 images
● Downy Mildew: 390 images
● Insects Infected: 431 images
● Healthy: 795 images
Total: 2,048 images
Augmented Classes:
● Black Spot: 4,320 images
● Downy Mildew: 3,900 images
● Insects Infected: 4,310 images
● Healthy: 7,950 images
Total Augmented: 20,480 images
To maximize its usefulness for research purposes, the dataset has undergone comprehensive preprocessing, including image classification, noise reduction, background removal, and augmentation. The augmented dataset significantly enhances the diversity and size of the original dataset, providing a robust foundation for training and testing machine learning models.

玫瑰作为爱情、纯洁与深情的象征，在全球范围内广受珍视，同时也是一类重要的经济作物。然而，多种叶部病害会显著影响其观赏价值与经济收益。为应对这一挑战，我们构建了一款专用的玫瑰叶片图像数据集，旨在推动病害识别相关的机器学习研究。

本数据集通过实地走访Golaap Gram、Allah Vorsha苗圃与Daffodil苗圃采集所得。所有图像均在自然光环境下拍摄，以确保画面清晰细腻、细节完整。数据集分为四个明确类别，涵盖原始版本与增强版本两类：

无增强版本：
● 黑斑病（Black Spot）：432张
● 霜霉病（Downy Mildew）：390张
● 虫害侵染（Insects Infected）：431张
● 健康叶片：795张
总计：2048张

增强类别：
● 黑斑病：4320张
● 霜霉病：3900张
● 虫害侵染：4310张
● 健康叶片：7950张
增强版总计：20480张

为最大化该数据集的科研应用价值，我们对其开展了全面的预处理操作，包括图像分类、降噪、背景移除与数据增强。增强后的数据集大幅提升了原始数据集的规模与多样性，可为机器学习模型的训练与测试提供坚实的支撑基础。

搜集汇总

背景与挑战

背景概述

该数据集专注于玫瑰叶片疾病检测，包含原始和增强版本，共四个类别：黑斑病、霜霉病、虫害感染和健康叶片，原始图像2,048张，增强后达20,480张。数据集通过实地采集在自然光下拍摄，并经过图像分类、降噪、背景去除和增强等预处理，旨在为机器学习模型提供多样化的训练和测试基础，以提升疾病识别研究效果。