辽宁富士苹果品质及病害近红外光谱数据集

doi:10.19788/j.issn.2096-6369.220205

农业大数据学报 ›› 2022, Vol. 4 ›› Issue (2): 30-35.doi: 10.19788/j.issn.2096-6369.220205

• 专题——果树育种数据 • 上一篇下一篇

辽宁富士苹果品质及病害近红外光谱数据集

张翔鹤^1,²(), 刘婷婷^1,^2,³, 王晓丽^1,^2,³, 胡林^1,², 樊景超^1,^2,³(), 姜林林⁴

^1.中国农业科学院农业信息研究所，北京 100081
^2.国家农业科学数据中心，北京 100081
^3.农业农村部农业大数据重点实验室，北京 100081
^4.保利国际生态环境治理（北京）有限公司，北京 100027

收稿日期:2022-03-22 出版日期:2022-06-26 发布日期:2022-11-08
通讯作者: 樊景超 E-mail:zhxianghe@163.com;fanjingchao@caas.cn
作者简介:[1] 张翔鹤|张翔鹤，女，硕士，研究方向：农业科学数据管理； E-mail: zhxianghe@163.com
基金资助:
中国农业科学院创新工程：数据整合与应用服务研究(2020CX017);中国国家重点研发计划(2022YFF0712100)

Near-infrared Spectral Data Set of Apple Quality and Disease of Fuji Apple in Liaoning Province

Xianghe Zhang^1,²(), Tingting Liu^1,^2,³, Xiaoli Wang^1,^2,³, Lin Hu^1,², Jingchao Fan^1,^2,³(), Linlin Jiang⁴

^1.Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China
^2.National Agriculture Science Data Center, Beijing 100081, China
^3.Key Laboratory of Big Agri-Data, Ministry of Agriculture, Beijing 100081, China
^4.Poly International Ecological Environment Management (Beijing) Co. , Ltd, Beijing 100027, China

Received:2022-03-22 Online:2022-06-26 Published:2022-11-08
Contact: Jingchao Fan E-mail:zhxianghe@163.com;fanjingchao@caas.cn

摘要/Abstract

摘要：

近年来，近红外光谱检测由于其有效性成为快速无损检测的首选方法。当前苹果果实近红外光谱数据的采集，仅限于苹果内部品质。而苹果果实易发生病害，其病害光谱数据意义重大，具有再次开发利用价值，有助于科研发现。本数据集通过对2010年辽宁兴城生产的富士苹果接种炭疽病菌和轮纹病菌，进行近红外光谱检测，收集相关数据，同时采集其原始品质近红外光谱数据作为对照。数据集包括苹果品质近红外光谱检测数据2150条，病害检测数据958条，数据保存格式为ASD文件，共计121MB。该数据集可为建立苹果果实病害的识别模型及无损检测方法研究提供数据基础。

关键词: 苹果, 果实病害, 近红外光谱, 无损检测

Abstract:

In recent years, near infrared spectroscopy (NIRS) has become the preferred method for rapid nondestructive testing due to its effectiveness. At present, the collection of near-infrared spectral data of apple fruits is limited to the internal quality of apples. However, apple fruit is prone to disease, and its disease spectral data is of great significance, which has the value of redevelopment and utilization, and is helpful for scientific research and discovery. In this data set, the Fuji apples produced in Xingcheng city of Liaoning Province in 2010 were inoculated with anthrax and Physalospora piricola, and the relevant data were collected by NIR spectroscopy. Meanwhile, the NIR data of the original quality was collected as the control. This data set includes 2150 pieces of near-infrared spectral detection data of apple quality and 958 pieces of disease detection data. The data storage format is ASD file, totaling 121MB. The data set can provide a data basis for the identification model of apple fruit diseases and the research of nondestructive testing methods.

Key words: apple, fruit disease, near infrared spectrum, nondestructive testing

中图分类号:

S432

张翔鹤, 刘婷婷, 王晓丽, 胡林, 樊景超, 姜林林. 辽宁富士苹果品质及病害近红外光谱数据集[J]. 农业大数据学报, 2022, 4(2): 30-35.

Xianghe Zhang, Tingting Liu, Xiaoli Wang, Lin Hu, Jingchao Fan, Linlin Jiang. Near-infrared Spectral Data Set of Apple Quality and Disease of Fuji Apple in Liaoning Province[J]. Journal of Agricultural Big Data, 2022, 4(2): 30-35.

图/表 7

图1

图2

图3

表1

图4

表2

参考文献 22

1	宋哲,王宏,里程辉,等.我国苹果产业存在的主要问题、发展趋势及解决办法[J].江苏农业科学,2016,44(09):4-8.
	Song Z, Wang H, Li C H, et al. The main problems, development trends and solutions of apple industry in China[J]. Jiangsu Agricultural Sciences, 2016, 44(09); 4-8.
2	常倩,李瑾.2000年以来中国苹果产业发展趋势分析[J].北方园艺,2021，(03):155-160.
	Chang Q, Li J. Analysis on the development trend of apple industry in China since 2000[J]. Northern Horticulture, 2021，(03): 155-160.
3	冯琦. 苹果园几种病虫害有机防控研究[D].保定:河北农业大学,2014.
	Feng Q. Study on organic control of several diseases and pests in apple orchard[D]. Baoding: Hebei Agricultural University, 2014.
4	宦克为. 小麦内在品质近红外光谱无损检测技术研究[D].长春:长春理工大学,2014.
	Huan K W. Near-infrared spectroscopy for nondestructive detection of wheat intrinsic quality[D]. Changchun: Changchun University of Science and Technology, 2014.
5	李水芳. 蜂蜜质量的近红外光谱分析技术研究[D].长沙:中南林业科技大学,2012.
	Li Y F. Study on Near Infrared Spectroscopy for Quality Analysis of Honey[D]. Changsha: Central South University Of Forestry And Technology, 2012.
6	Martínez-Ferri E et al. Nondestructive Detection of White Root Rot Disease in Avocado Rootstocks by Leaf Chlorophyll Fluorescence.[J]. Plant disease, 2016, 100(1) : 49-58.
7	Takashi SHIMOMACHI et al. Nondestructive Detection of Water Stress in Tomato Plants by Microwave Sensing[J]. Environment Control in Biology, 2001, 39(1) : 17-25.
8	Shilyaev P . al V.et. Track Record of Nondestructive Testing Methods Applied to Steel Plate Quality Indexes at Magnitogorsk Iron and Steel Company[J]. Metallurgist, 2021, 64(11-12) : 1234-1238.
9	赵卓. 全自动水果分拣系统的设计与开发[D].长沙:湖南师范大学,2020.
	Zhao Z. Design and development of automatic fruit sorting system[D]. Changsha: Hunan Normal University, 2020.
10	马竞一. 玉米单倍体籽粒的自动分拣技术研究与实现[D].青岛:中国石油大学(华东),2015.
	Ma J Y. Research and realization of automatic sorting technology for maize haploid seeds[D]. Qingdao: China University of Petroleum (East China), 2015.
11	郭志明. 基于近红外光谱及成像的苹果品质无损检测方法和装置研究[D].北京:中国农业大学,2015.
	Guo Z M. Research on Nondestructive Testing Method and Device of Apple Quality Based on Near Infrared Spectroscopy and Imaging[D]. Beijing: China Agricultural University, 2015.
12	王梓萌. 基于近红外光谱与密度特征的苹果霉心病无损检测方法研究[D].保定:河北农业大学,2020.
	Wang Z M. Near-infrared spectroscopy and density characteristics for nondestructive detection of mallea mallea heart disease[D]. Baoding: Hebei Agricultural University, 2020.
13	史波林,赵镭,刘文,等.苹果内部品质近红外光谱检测的异常样本分析[J].农业机械学报,2010,41(02):132-137.
	Shi B L, Zhao L, Liu W,et al. Analysis of abnormal samples of apple internal quality detected by near infrared spectroscopy[J]. Transactions of The Chinese Society of Agricultural Machinery, 2010, 41(02): 132-137.
14	徐海. 苹果糖度可见/近红外漫透射光谱在线检测通用模型研究[D].南昌:华东交通大学,2020.
	Xu H. A general model for online measurement of apple sugar content by visible/near-infrared diffuse transmission spectroscopy[D]. Nanchang: East China Jiaotong University, 2020.
15	尹秀珍. 低分辨率苹果果实病害图像识别方法研究[D].咸阳:西北农林科技大学,2011.
	Yin X Z. Research on low resolution image recognition of apple fruit disease[D]. Xianyang: Northwest A&F University, 2011.
16	黄天祥. 2016-2020年河南苹果主要病虫害预测预警技术数据集. CSTR:17058.11.A0002.20210706.30.ds.0428.
	Huang T X. Prediction and early warning technology data set of major Apple diseases and insect pests in Henan from 2016 to 2020. CSTR:17058.11.A0002.20210706.30.ds.0428.
17	王晓丽.2016年辽宁兴城‘富士’、‘华红’、‘嘎啦’苹果叶片光谱与图像数据集. CSTR:17058.11.A0007.20211029.36.is.1909.
	Wang X L. Spectral and image data set of ‘Fuji’, ‘Huahong’ and ‘Gala’ Apple leaves in Xingcheng, Liaoning province in 2016. CSTR:17058.11.A0007.20211029.36.is.1909.
18	张晴,蔡贵民,陈斌,等.光栅型高速扫描近红外光谱仪的研发[J].分析仪器,2018，(03):29-34.
	Zhang Q, Cai G M, Chen B,et al. Development of grating type high speed scanning near infrared spectrometer[J]. Analytical Instrumentation, 2018，(03): 29-34.
19	樊景超. 苹果果实病害近红外光谱信息获取与识别模型研究[D].北京: 中国农业科学院,2011.
	Fan J C. Study on Near Infrared Spectral Information Acquisition and Recognition Model of Apple Fruit Diseases[D]. Beijing: Chinese Academy of Agricultural Sciences, 2011.
20	刘言,蔡文生,邵学广.近红外光谱分析方法研究:从传统数据到大数据[J].科学通报,2015,60(08):704-713.
	Liu Y, Cai W S, Shao X G. Research on Near Infrared Spectroscopic Analysis Methods: From Traditional Data to Big Data[J]. Chinese Science Bulletin, 2015, 60(08): 704-713.
21	褚小立,史云颖,陈瀑,等.近五年我国近红外光谱分析技术研究与应用进展[J].分析测试学报,2019,38(05):603-611.
	Chu X L, Shi Y Y, Chen P,et al. Progress in the research and application of near infrared spectroscopy in China in the past five years[J]. Journal of Instrumental Analysis, 2019, 38(05): 603-611.
22	邹小波,赵杰文.用遗传算法快速提取近红外光谱特征区域和特征波长[J].光学学报,2007，(07):1316-1321.
	Zou X B, Zhao J W. The genetic algorithm is used to quickly extract the characteristic region and characteristic wave of near infrared spectrum[J]. Acta Optica Sinica, 2007，(07): 1316-1321.

数据库（集）名称	辽宁富士苹果品质及病害近红外光谱数据集
数据作者及分工	樊景超：光谱和图像数据采集，总体方案设计与组织实施；张翔鹤：数据汇总整理及论文撰写；刘婷婷：数据整理与分类汇总；王晓丽：实验材料处理与数据整理；胡林：总体方案设计与组织实施；姜林林：光谱和图像数据采集
通讯作者及邮箱	樊景超，E-mail:fanjingchao@caas.cn
数据时间范围	2010年
采集区域	中国辽宁兴城
数据量	121MB
数据格式	.asd
数据服务系统网址	https://www.agridata.cn/data.html#/datadetail?id=286639
基金项目	中国农业科学院创新工程：数据整合与应用服务研究（2020CX017)；中国国家重点研发计划（2022YFF0712100）
数据库（集）组成	数据集主要包含富士苹果果实品质光谱数据，以及果实病害（炭疽病菌和轮纹病菌）光谱数据，其中： 1. 果实病害数据，958条，45.8MB； 2. 果实品质数据，2150条，75.2MB。

型号	波长范围	光谱采样间隔	光谱分辨率（FWHM）
FieldSpec3 VNIR	350-1025nm	1.377nm	3nm@700nm
Field Spec 3 JR	350-2500nm	1.377nm@350-1050nm； 2nm@1000-2500nm	3nm@700nm； 30nm@1400nm,2100nm
FieldSpec3	350- 2500nm	1.377nm@350-1050nm 2nm@1000-2500nm	3nm@700nm； 10nm@1400nm,2100nm
Field Spec 3 Hi-Res	350- 2500nm	1.377nm@350-1050nm； 2nm@1000-2500nm	3nm@700nm 8.5nm@1400nm 6.5nm@2100nm

苹果类型	样品数	发病数	发病率	发病时间
A轮纹病	30	30	100%	2
B炭疽病	30	30	100%	2
C清水对照	30	0	0%	无

[1]	匡立学, 徐国锋, 陈志军, 李银萍, 程杨, 沈友明. 2017年中国‘富士’苹果品质数据集[J]. 农业大数据学报, 2022, 4(3): 49-53.
[2]	李燕青, 李壮, 杨安, 程存刚, 张凝. 2018年辽宁绥中县苹果园土壤养分特征数据集[J]. 农业大数据学报, 2022, 4(3): 60-63.
[3]	岳强, 闫文涛, 刘池林, 王春田, 孙丽娜, 张怀江, 仇贵生. 2019—2021年辽宁苹果产区桃小食心虫成虫发生动态数据集[J]. 农业大数据学报, 2022, 4(3): 73-76.
[4]	孙思邈, 高源, 王昆, 王大江, 李连文. 23份苹果种质资源的果实品质分析[J]. 农业大数据学报, 2022, 4(2): 13-19.
[5]	贾朝爽, 王志华. 不同产地的12个苹果品种品质指标数据集[J]. 农业大数据学报, 2022, 4(2): 20-24.
[6]	胡国君, 董雅凤, 张尊平, 范旭东, 任芳. 用于苹果树病毒鉴定的small RNA深度测序数据集[J]. 农业大数据学报, 2022, 4(2): 25-29.
[7]	高飞, 王晓丽, 胡林, 樊景超, 刘婷婷, 闫燊, 曹姗姗. 2016年辽宁兴城富士、华红、嘎啦苹果叶片光谱与图像数据集[J]. 农业大数据学报, 2022, 4(1): 109-113.
[8]	王晓丽, 胡乾浩, 满芮, 刘婷婷. 中国果树花期2016年近红外光谱和图像数据集[J]. 农业大数据学报, 2021, 3(1): 88-93.
[9]	王晓丽, 胡乾浩, 樊景超, 李壮. 辽北苹果叶片氮含量、近红外光谱与图像数据集[J]. 农业大数据学报, 2020, 2(4): 113-119.
[10]	高飞, 王晓丽, 刘婷婷, 李壮, 满芮. 中国苹果叶片病虫害2015年光谱和图像数据集[J]. 农业大数据学报, 2020, 2(4): 120-124.

辽宁富士苹果品质及病害近红外光谱数据集

Near-infrared Spectral Data Set of Apple Quality and Disease of Fuji Apple in Liaoning Province

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 22

相关文章 10

Metrics

本文评价

推荐阅读 0