水稻叶片病害数据集

Jitesh P. Shah, Email: jitesh2k12 '@' gmail.com, Institute: Department of Information Technology, Dharmsinh Desai University,Nadiad-387001, Gujarat, INDIA. Creator Name: Harshadkumar B. Prajapati, Email: prajapatihb.it '@' ddu.ac.in, Institute: Department of Information Technology, Dharmsinh Desai University,Nadiad-387001, Gujarat, INDIA. Creator Name: Vipul K. Dabhi, Email: vipuldabhi.it '@' ddu.ac.in, Institute: Department of Information Technology, Dharmsinh Desai University,Nadiad-387001, Gujarat, INDIA. Data Set Information: The dataset was created by manually separating infected leaves into different disease classes. We had consulted the farmers and had asked them to provide names of diseases for sample leaves. Farmers had provided names in their native languages (Gujarati) and we identi???ed and veri???ed English names of those diseases by consulting with experts of agriculture ???eld. This dataset was used for Detection and Classi???cation of Rice Plant Diseases. As part of the work, the following activities were carried out (1) How to extract various image features (2) which image processing operations can provide needed information (3) which image features can provide substantial input for classification. The survey work is available in IEEE conference paper: A Survey on Detection and Classification of Rice Plant Diseases, available at [Web link]. A classification model was developed using SVM. The detailed information is available in the published journal article:Detection and classification of rice plant diseases, in Intelligent Decision Technologies, IOS Press, available at [Web link] Attribute Information: Image Format: .jpg, The images were captured with a white background, in direct sunlight. The images were reduced to the desired resolution for processing. Relevant Papers: (1) Prajapati HB, Shah JP, Dabhi VK. Detection and classification of rice plant diseases. Intelligent Decision Technologies. 2017 Jan 1;11(3):357-73, doi: 10.3233/IDT-170301. (2) Shah JP, Prajapati HB, Dabhi VK. A survey on detection and classification of rice plant diseases. InCurrent Trends in Advanced Computing (ICCTAC), IEEE International Conference on 2016 Mar 10 (pp. 1-8). IEEE. Citation Request: Prajapati HB, Shah JP, Dabhi VK. Detection and classification of rice plant diseases. Intelligent Decision Technologies. 2017 Jan 1;11(3):357-73, doi: 10.3233/IDT-170301.

Jitesh P. Shah，电子邮箱：jitesh2k12 '@' gmail.com，所属机构：印度古吉拉特邦讷迪亚德市Dharmsinh Desai大学信息技术系，邮编387001。
数据集创建者：Harshadkumar B. Prajapati，电子邮箱：prajapatihb.it '@' ddu.ac.in，所属机构：印度古吉拉特邦讷迪亚德市Dharmsinh Desai大学信息技术系，邮编387001。
数据集创建者：Vipul K. Dabhi，电子邮箱：vipuldabhi.it '@' ddu.ac.in，所属机构：印度古吉拉特邦讷迪亚德市Dharmsinh Desai大学信息技术系，邮编387001。

### 数据集说明
本数据集通过人工将染病水稻叶片划分至不同病害类别构建而成。研究团队咨询当地农户，邀请其提供样本叶片对应的病害名称，农户以母语古吉拉特语给出病害名称，随后团队通过咨询农业领域专家，对上述病害的英文名称进行了确认与核验。本数据集曾用于水稻植物病害的检测与分类任务。
作为本研究的一部分，团队开展了以下相关工作：(1) 如何提取各类图像特征；(2) 哪些图像处理操作可获取所需的图像信息；(3) 哪些图像特征可为分类任务提供有效输入。
相关综述研究已发表于IEEE会议论文《水稻植物病害检测与分类综述》，可通过[Web link]获取。
研究采用支持向量机（Support Vector Machine, SVM）构建了分类模型，详细研究内容已发表于期刊论文《水稻植物病害的检测与分类》，刊载于IOS Press旗下的《智能决策技术》期刊，可通过[Web link]获取。

### 属性信息
图像格式：.jpg，图像采集于直射阳光下，背景为纯白色。所有图像均被调整至处理所需的分辨率。

### 相关论文
1. Prajapati HB, Shah JP, Dabhi VK. 水稻植物病害的检测与分类. 《智能决策技术》, 2017年1月1日; 11(3): 357-373, DOI: 10.3233/IDT-170301.
2. Shah JP, Prajapati HB, Dabhi VK. 水稻植物病害检测与分类综述. 收录于2016年IEEE国际先进计算前沿趋势会议（ICCTAC）论文集, 2016年3月10日, 第1-8页. IEEE出版.

### 引用要求
请引用以下文献：Prajapati HB, Shah JP, Dabhi VK. 水稻植物病害的检测与分类. 《智能决策技术》, 2017年1月1日; 11(3): 357-373, DOI: 10.3233/IDT-170301.

搜集汇总

背景与挑战

背景概述

水稻叶片病害数据集是一个专注于水稻植物病害检测和分类的图像数据集，由研究人员手动分离受感染叶片并咨询农民和专家确认病害类别创建。数据集包含.jpg格式图像，在白色背景和直射阳光下拍摄，适用于计算机视觉和农业智能研究，并已用于支持相关学术论文的发表。