高光谱成像技术在作物种子方面的应用

doi:10.6046/gtzyyg.2020.04.04

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摘要

作物种子作为种植业最基本、最原始的生产资料,选择出高质量的种子直接决定着农业生产的经济效益和生产效益。高光谱成像技术出现于20世纪80年代,具有无损、快速成像以及“图谱合一”等特点。运用高光谱成像技术在作物种子方面的研究,前人主要集中于作物种子的品种鉴别、活力检测和种子品质检测等方面。在前人的研究基础上进行深化总结凝炼可知,高光谱成像在作物种子品种鉴别研究主要应用数据处理模型包括偏最小二乘法(partial least squares,PLS)、 Ada-Boost算法、极限学习机(extreme learning machine,ELM)、随机森林(random forest,RF)、支持向量机(support vector machine,SVM)和人工神经网络(artificial neural network,ANN)等。综上所述,本研究旨在为各种类型的作物种子研究提供最佳的光谱范围、样本种类、降噪方法、特征波段提取和模型建立等方面的依据,且对未来研究的方向提供了建议。

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	彭晓伟
	张爱军
	王楠
	赵丽

关键词 ：光谱成像技术, 作物种子, 特征波段, 检测

Abstract：

Crop seeds are the most basic and original means of production in the planting industry. The selection of high-quality seeds directly determines the economic and production benefits in the agricultural production process. Hyperspectral imaging technology emerged in the 1980s, which has the characteristics of non-destruction, rapid imaging and “integration of atlas”. Previous studies of crop seeds using hyperspectral imaging technology mainly focused on the variety identification, vigor detection, and seed quality of crop seeds. In this paper, based on the previous research, the authors summarize and refine the data processing models, which include such methods as partial least square method, Ada-Boost algorithm, limit learning machine (ELM), random forest (RF), support vector machine (SVM), and artificial neural network (ANN). To sum up, the purpose of this paper is to provide the best spectral range, sample types, noise reduction methods, feature band extraction, model building and other aspects as the basis for various types of crop seed research, and to provide suggestions for future research direction.

Key words： spectral imaging technology crop seeds characteristic band detection

收稿日期: 2019-12-10 出版日期: 2020-12-23

:	S127
	TP79

基金资助:河北省重点研发计划项目“基于无人机高光谱遥感的河北省山区谷子生长特征反演建模与品质提升关键技术研究”(19226421D)

通讯作者: 张爱军

作者简介: 彭晓伟(1997-),男,硕士研究生,主要从事高光谱技术在农业的应用研究。Email:1187846870@qq.com。

引用本文:

彭晓伟, 张爱军, 王楠, 赵丽. 高光谱成像技术在作物种子方面的应用[J]. 国土资源遥感, 2020, 32(4): 23-32.
PENG Xiaowei, ZHANG Aijun, WANG Nan, ZHAO Li. Application of hyperspectral imaging technology in crop seeds. Remote Sensing for Land & Resources, 2020, 32(4): 23-32.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.04.04 或 https://www.gtzyyg.com/CN/Y2020/V32/I4/23

Tab.1 高光谱成像在种子活力检测中的应用研究概况

年份	测定内容	光谱仪	光谱仪产地	波长/nm
2008年	小麦的品种鉴别	Foss-NIRSystem 6500	美国	400~2 500
2017年	小麦的品种鉴别	ICL-B1610M-SC000	美国	388~1 000
2019年	小麦的品种鉴别	PFD-65-V10E	芬兰	900~1 700
2012年	玉米的品种鉴别	1003A-10140HyperspcTMVNIRC-Series	美国	563.6~911.4
2014年	玉米的品种鉴别	1003A-10140HyperspcTMVNIRC-Series	美国	400~1 000
2008年	水稻的品种鉴别	Handheld FieldSpec	美国	350~1 075
2013年	水稻的品种鉴别	ImspectorN17E	芬兰	1 039~1 612
2015年	水稻的品种鉴别	1003A-10140HyperspcTMVNIRC-Series	美国	400~1 000
2016年	水稻的品种鉴别	RT100-HP激光诱导击穿光谱仪	美国	190~1 040
2018年	水稻的品种鉴别	XDS Rapid Content TM	丹麦	400~2 498
2011年	油菜籽的品种鉴别	ImSpectorV10E-QE	芬兰	400~1 000
2013年	西瓜种子的品种鉴别	ImSpectorN17E-QE	芬兰	800~1 800
2014年	大豆的品种鉴别	ImSpectorV25E	芬兰	1 000~2 500
2014年	大白菜的品种鉴别	ImSpectorN17E-XLNIR	芬兰	874~1 734
2011年	大麦、小麦、高粱的种子活力	ImSpectorN17E-QE	芬兰	1 000~2 498
2018年	小麦的种子活力	ImSpectorV10E	芬兰	400~1 000
2019年	小麦的种子活力	ImSpectorV10E	芬兰	400~970
2013年	玉米的种子活力	VECTOR22/N	德国	833~2 500
2016年	玉米的种子活力	HeadwellPhotonics,Fitchburg,MA	美国	400~2 500
2018年	玉米的种子活力	HeadwallPhotonics,Fitchburg,MA	美国	1 000~2 500
2018年	玉米的种子活力	赛默飞(Antaris)II型傅立叶变换近红外光谱仪	美国	1 000~2 500
2015年	水稻的种子活力	ImSpectorV10E-QE	芬兰	400~1 000
2016年	水稻的种子活力	ImSpectorN17E-QE	芬兰	874~1 740
2017年	水稻的种子活力	ImSpectorN17E	芬兰	874~1 734
2017年	脱绒棉种的种子活力	ImSpectorV10E -QE	芬兰	400~1 000
2018年	脱绒棉种的种子活力	ImSpectorV10E -QE	芬兰	400~1 000
2003年	松树的种子活力	FOSS Tecator	瑞典	850~1 048
年份	测定内容	光谱仪	光谱仪产地	波长/nm
2003年	毛榉树的种子活力	FOSS NIR Systems	美国	400~2 498
2014年	辣椒的种子活力	VNIR,HeadwallPhotonics,Fitchburg,MA	美国	400~700
2015年	黄瓜的种子活力	VNIR,HeadwallPhotonics,Fitchburg,MA	美国	425~700
2016年	甜瓜的种子活力	SWIR,HeadwallPhotonics,Fitchburg,MA	美国	948~2 494
2009年	大麦、小麦、玉米的杂质检测	SpecimV10 spectrograph	芬兰	400~950
2010年	小麦的受损种子	ImSpectorV10E	芬兰	400~1 000
2011年	小麦的镰刀菌	VNIR100E	美国	400~1 000
2015年	小麦的镰刀菌	EV/NIR HyperspecModel 1003B-10151	美国	528~1 785
2015年	小麦的灰绿曲霉、青霉、赭曲霉毒素	Model No.SU640-1.7RT-D	美国	1 000~1 600
2012年	玉米的冻害种子	PertenDA7200	瑞典	860~1 640
2016年	玉米的受损种子	ImSpectorV10E-QE,ImSpectorN25E	芬兰	400~1 000
2009年	玉米的镰刀菌	SWIR	芬兰	960~1 662
2010年	玉米的种子霉变	ImSpector-SpecimV10	芬兰	400~1 000
2010年	玉米的黄曲霉毒素	AvaSpec-ULS2048-USB2	荷兰	400~1 000
2014年	玉米的黄曲霉毒素	SpecimV10MspectrographOulu	芬兰	400~1 000
2017年	玉米的种子真菌侵染	ImspectorV10E	芬兰	400~1 000
2004年	玉米的含油率和含水率	ApogeeKX-260 Monochrome Scientific Camera	美国	750~1 090
2006年	玉米的油分和油酸	Matrix NIR	美国	950~1 700
2009年	玉米的粗蛋白、色氨酸、赖氨酸、油分和可溶性糖	CDI Spectrometer	美国	904~1 685
2015年	水稻的种子霉变	JFE,Techno-ResearchCorporation	日本	400~1 000
2006年	油菜、鹰嘴豆、芥菜的纤维、脂肪酸组成、蛋白质和油脂	NIR Systems Model6500 Spectrophotometer	美国	400~2 500
2014年	油茶的脂肪酸、油酸、亚油酸和棕榈酸	FieldSpecHH2	美国	325~1 075

Tab.2 作物种子的测定内容及使用光谱仪类型

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