柑橘黄龙病田间症状识别图像数据集  

A dataset of field symptoms for identifying Citrus Huanglongbing

作　　者：池美香[1] 陈韶萍 黄婷 陈世雄 梁勇 邱荣洲[1] CHI Meixiang;CHEN Shaoping;HUANG Ting;CHEN Shixiong;LIANG Yong;QIU Rongzhou(Fujian Key Laboratory for Monitoring and Integrated Management of Crop Pests,Institute of Plant Protection,Fujian Academy of Agricultural Sciences,Fuzhou 350013,P.R.China;Institute of Digital Agriculture,Fujian Academy of Agricultural Sciences,Fuzhou 350003,P.R.China)

机构地区：[1]福建省农业科学院植物保护研究所,福建省作物有害生物监测与治理重点实验室,福州350003 [2]福建省农业科学院数字农业研究所,福州350003

出　　处：《中国科学数据(中英文网络版)》2025年第1期45-54,共10页China Scientific Data

基　　金：国家重点研发项目(2021YFD1400800);“5511”协同创新工程(XTCXGC2021011、XTCXGC2021017);福建省农业科学院植物保护创新团队项目(CXTD2021027)。

摘　　要：柑橘黄龙病的快速识别有助于生产者尽早发现并清除柑橘黄龙病植株。基于深度学习的植物病害图像自动识别依赖于庞大的数据集,然而开放的可用于柑橘黄龙病识别的数据集非常有限。本研究通过手机或相机拍摄,整理了一组包含自然生态、白色背景板和黑色背景板3种采集环境的照片数据集,收录了包含黄龙病及相关病害症状共14种类别的图片9896张。其中,训练集样本7192张,验证集样本898张,测试集样本1806张(其中602个样本具有PCR检测结果)。样本的症状类别均由植保专家进行分类和标注以确定数据的质量和可靠性。本数据集提供了yolo格式(txt)的标注文件和基于Yolov5l训练获得的模型文件,以期为基于YOLO卷积神经网络的植物病害分类、目标检测和自动识别领域的机器学习建模提供训练和测试样本。The rapid identification of Citrus Huanglongbing can help producers to detect and eliminate infected plants as early as possible.The automatic recognition of plant diseases based on deep learning relies on large datasets,but there are few open datasets available for citrus huanglongbing recognition.In this study,a datasets of images was collected using a mobile phone or camera,involving three different collection environments:natural environment,white background,and black background.The dataset includes a total of 9,896 images including 14 categories of symptoms related to Huanglongbing and other associated diseases.Among these,7,192 samples were used for the training set,898 for the validation set,and 1,806 for the test set(of which 602 had PCR test results).The symptom categories of the samples were classified and annotated by plant detection experts to ensure the quality and reliability of the data.The datasets presents a YOLO format(txt)annotation file and model files trained using YOLOv51,aiming to provide training and testing samples for developing models of machine learning based on the YOLO convolutional neural network in the fields of plant disease classification,object detection and automatic recognition.

关 键 词：黄龙病 柑橘病害 症状识别 图像识别 YOLO 数据集 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S436.66[自动化与计算机技术—计算机科学与技术]