Hyperspectral imaging technology combined with multivariate data analysis to identify heat-damaged rice seeds-Reference-Cited by-同舟云学术

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Hyperspectral imaging technology combined with multivariate data analysis to identify heat-damaged rice seeds

Published:2020-02-12 Issue:3 Volume:53 Page:207-221
ISSN:0038-7010
Container-title:Spectroscopy Letters
language:en
Short-container-title:Spectroscopy Letters

Author:

Zhang Liu¹²^ORCID,Rao Zhenhong³,Ji Haiyan¹²

Affiliation:

1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing, China;

2. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China;

3. College of Science, China Agricultural University, Beijing, China

Publisher

Informa UK Limited

Subject

Spectroscopy,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/00387010.2020.1726402

Reference39 articles.

1. Near Infrared Hyperspectral Imaging for White Maize Classification According to Grading Regulations

2. Detection of sprout damage in wheat kernels using NIR hyperspectral imaging

3. Identification of transgenic foods using NIR spectroscopy: A review

4. Detection of Broken Kernels Content in Bulk Wheat Samples Using Near-Infrared Hyperspectral Imaging

5. Detection of sprout damage in Canada Western Red Spring wheat with multiple wavebands using visible/near-infrared hyperspectral imaging

Cited by 18 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hyperspectral Non-Destructive Detection of Heat-Damaged Maize Seeds;SPECTROSC SPECT ANAL;2024

2. Classification of the physiological potential of soybean seed lots using infrared spectroscopy and chemometric methods;Journal of Seed Science;2024

3. SAM-GAN: An improved DCGAN for rice seed viability determination using near-infrared hyperspectral imaging;Computers and Electronics in Agriculture;2024-01

4. Integration of Spectral and Image Data from Hyperspectral Imaging for the Improved Classification of Unsound Wheat Kernels Combining Convolutional Neural Network;2024

5. Precision opto-imaging techniques for seed quality assessment: prospects and scope of recent advances;Remote Sensing in Precision Agriculture;2024

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