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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (4) : 23-32     DOI: 10.6046/gtzyyg.2020.04.04
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Application of hyperspectral imaging technology in crop seeds
PENG Xiaowei1(), ZHANG Aijun1,2(), WANG Nan3, ZHAO Li4
1. College of Resources and Environment Science, Agricultural University of Hebei,Baoding 071001, China
2. Hebei Mountain Research Institute, Baoding 071000, China
3. College of Mechanical and Electrical Engineering, Agricultural University of Hebei, Baoding 071000, China
4. College of Land and Resources, Agricultural University of Hebei, Baoding 071000, China
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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.

Keywords spectral imaging technology      crop seeds      characteristic band      detection     
:  S127  
  TP79  
Corresponding Authors: ZHANG Aijun     E-mail: 1187846870@qq.com;xm70526@163.com
Issue Date: 23 December 2020
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Xiaowei PENG
Aijun ZHANG
Nan WANG
Li ZHAO
Cite this article:   
Xiaowei PENG,Aijun ZHANG,Nan WANG, et al. Application of hyperspectral imaging technology in crop seeds[J]. Remote Sensing for Land & Resources, 2020, 32(4): 23-32.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.04.04     OR     https://www.gtzyyg.com/EN/Y2020/V32/I4/23
波段 种子类别 波段采集
范围/nm
建模方法 参考文献
可见光
波段
辣椒 600~700 PLS-DA Mo等[34]
黄瓜 425~700 多光谱算法 Mo等[35]
小麦 430~970 PLS-DA 张婷婷等[36]
可见光—短波近红外波段 松树 850~1 048 PLS-DA Tigabu等[37]
水稻 400~1 000 SVM 李美凌等[38]
脱绒棉 400~1 000 SVM 尤佳[39]
脱绒棉 400~1 000 PLS-DA 黄蒂云[40]
毛榉树种 400~2 498 PLS-DA Soltani等[41]
玉米 400~2 500 PLS-DA Ambrose等[42]
小麦 400~1 000 PLS-DA Zhang等[43]
长波近红外波段 大麦、小麦、高粱 1 000~2 498 PLS-DA McGoverin等[44]
甜瓜 948~2 494 PLS-DA Kandpal等[45]
玉米 833~2 500 神经网络 杨冬风等[46]
玉米 1 000~2 500 SVM Wakholi等[47]
玉米 1 000~2 500 PLS-DA Ambrose等[48]
水稻 874~1 740 PLS-DA 许思等[49]
水稻 874~1 734 SVM 吴小芬等[50]
Tab.1  Summary of the application of hyperspectral imaging in seed vigor detection
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  Determination of crop seeds and use of spectrometer type
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