cut-flower disease dataset

This dataset is a dataset that prepared for cut-flower disease detection and classiffication by using computer vision. as it is known cut-flowers are flowers or flower buds that have been cut from the plant bearing it for decorative purpose. it has a great role in balancing a gross domestic product(GDP) of the developing countries like Ethiopia and Kenya. however, it has a big impact on balancing economy, the countries cannot achieve the maximum effort and income due to disease problem. so this dataset is prepared for supporting a researcher those need to support a farmer and investor.the dataset is an image dataset that contain a cut-flowers leaf diseases. black-spot, powdery-mildew, downy-mildew and normal cut-flower leaves are included. in my research that I proposed in Addisababa science and Technology university for partial fulfillment of MSc degree, "developing deep-learning based cut-flower disease detection and classification model " is done by this dataset and achieve a good result in evaluattion matrix. this dataset is prepared through image collecting , image analysis and image labelling procedure. in image collecting(gathering) level the scholar, expert(plant pathologists) and plant managers are participated. a mobile with 64 mpx used for capturing cut-flower leaves image. a distance between a leaves and mobile camera averegically is 30 yards and also in image analysis the blurred image, the image hidden by other leaves, the image captured from a more distance which is not visible is outed during images analysis. finally the images are labelled by using LabelImg tool with their correspondence name. the labelling intension is for multi-label classification which has localization.本数据集专为基于计算机视觉（Computer Vision）的切花（cut-flower）病害检测与分类任务构建。众所周知，切花指为装饰用途从植株上剪切下的花朵或花骨朵。切花产业对埃塞俄比亚、肯尼亚等发展中国家的国内生产总值（Gross Domestic Product, GDP）平衡具有关键贡献，但病害问题严重制约了这些国家获取最大化经济收益与产业发展成效。因此本数据集旨在为致力于帮扶农户与投资者的研究人员提供支持。 本数据集为图像数据集，涵盖切花叶片病害样本，包含黑斑病、白粉病、霜霉病叶片以及健康切花叶片四类样本。本数据集曾用于作者在亚的斯亚贝巴科学技术大学为满足理学硕士学位毕业要求所开展的研究课题——“基于深度学习的切花病害检测与分类模型构建”，并在评估指标中取得了优异结果。 本数据集通过图像采集、图像分析与图像标注三个流程构建完成。在图像采集环节，学者、植物病理学家以及种植管理人员共同参与，采用配备6400万像素摄像头的智能手机拍摄切花叶片图像，拍摄时叶片与手机摄像头的平均距离约为30码（约27.43米）。在图像分析阶段，模糊图像、被其他叶片遮挡的图像以及拍摄距离过远导致细节无法辨识的图像均被剔除。最终，研究团队使用LabelImg标注工具对图像进行标注，标注名称与样本类别一一对应，本次标注旨在支持具备定位功能的多标签分类任务。