• Laser & Optoelectronics Progress
  • Vol. 56, Issue 11, 113001 (2019)
Libo Rao1, Tao Pang1, Ranshi Ji1, Xiaoyan Chen2、3、*, and Jie Zhang2
Author Affiliations
  • 1 College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, Sichuan 625014, China
  • 2 College of Information Engineering, Sichuan Agricultural University, Yaan, Sichuan 625014, China
  • 3 Sichuan Provincial Key Laboratory of Agricultural Information Engineering, Sichuan Agricultural University, Yaan, Sichuan 625014, China
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    DOI: 10.3788/LOP56.113001 Cite this Article Set citation alerts
    Libo Rao, Tao Pang, Ranshi Ji, Xiaoyan Chen, Jie Zhang. Firmness Detection for Apples Based on Hyperspectral Imaging Technology Combined with Stack Autoencoder-Extreme Learning Machine Method[J]. Laser & Optoelectronics Progress, 2019, 56(11): 113001 Copy Citation Text show less
    References

    [1] Wei W S, Peng Y K, Zheng X C et al. Rapid determination of content of total volatile basic nitrogen in pork based on multispectral detection system with optimal wavelength[J]. Acta Optica Sinica, 37, 1130003(2017).

         Wei W S, Peng Y K, Zheng X C et al. Rapid determination of content of total volatile basic nitrogen in pork based on multispectral detection system with optimal wavelength[J]. Acta Optica Sinica, 37, 1130003(2017).

    [2] Lu R F. Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images[J]. Sensing and Instrumentation for Food Quality and Safety, 1, 19-27(2007). http://link.springer.com/article/10.1007/s11694-006-9002-9

         Lu R F. Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images[J]. Sensing and Instrumentation for Food Quality and Safety, 1, 19-27(2007). http://link.springer.com/article/10.1007/s11694-006-9002-9

    [3] Zhu Q B, Huang M, Zhao X et al. Wavelength selection of hyperspectral scattering image using new semi-supervised affinity propagation for prediction of firmness and soluble solid content in apples[J]. Food Analytical Methods, 6, 334-342(2013). http://link.springer.com/article/10.1007/s12161-012-9442-2

         Zhu Q B, Huang M, Zhao X et al. Wavelength selection of hyperspectral scattering image using new semi-supervised affinity propagation for prediction of firmness and soluble solid content in apples[J]. Food Analytical Methods, 6, 334-342(2013). http://link.springer.com/article/10.1007/s12161-012-9442-2

    [4] Li R, Fu L S. Nondestructive measurement of firmness and sugar content of blueberries based on hyperspectral imaging[J]. Transactions of the Chinese Society of Agricultural Engineering, 33, 362-366(2017).

         Li R, Fu L S. Nondestructive measurement of firmness and sugar content of blueberries based on hyperspectral imaging[J]. Transactions of the Chinese Society of Agricultural Engineering, 33, 362-366(2017).

    [5] Li H D, Liang Y Z, Xu Q S et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J]. Analytica Chimica Acta, 648, 77-84(2009). http://www.ncbi.nlm.nih.gov/pubmed/19616692

         Li H D, Liang Y Z, Xu Q S et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J]. Analytica Chimica Acta, 648, 77-84(2009). http://www.ncbi.nlm.nih.gov/pubmed/19616692

    [6] Wang C P, Huang W Q, Fan S X et al. Moisture content detection of maize kernels based on hyperspectral imaging technology and CARS[J]. Laser & Optoelectronics Progress, 53, 123001(2016).

         Wang C P, Huang W Q, Fan S X et al. Moisture content detection of maize kernels based on hyperspectral imaging technology and CARS[J]. Laser & Optoelectronics Progress, 53, 123001(2016).

    [7] Zhang B H, Fan S X, Li J B et al. Detection of early rottenness on apples by using hyperspectral imaging combined with spectral analysis and image processing[J]. Food Analytical Methods, 8, 2075-2086(2015). http://link.springer.com/article/10.1007/s12161-015-0097-7

         Zhang B H, Fan S X, Li J B et al. Detection of early rottenness on apples by using hyperspectral imaging combined with spectral analysis and image processing[J]. Food Analytical Methods, 8, 2075-2086(2015). http://link.springer.com/article/10.1007/s12161-015-0097-7

    [8] Gao H Y, Mao H P, Zhang X D. Measurement of nitrogen content in lettuce canopy using spectroscopy combined with BiPLS-GA-SPA and ELM[J]. Spectroscopy and Spectral Analysis, 36, 491-495(2016).

         Gao H Y, Mao H P, Zhang X D. Measurement of nitrogen content in lettuce canopy using spectroscopy combined with BiPLS-GA-SPA and ELM[J]. Spectroscopy and Spectral Analysis, 36, 491-495(2016).

    [9] Wang S T, Wang X L, Chen D Y et al. Application of GA-BP neural network in detection of trace phosphate[J]. Chinese Journal of Lasers, 42, 0515001(2015).

         Wang S T, Wang X L, Chen D Y et al. Application of GA-BP neural network in detection of trace phosphate[J]. Chinese Journal of Lasers, 42, 0515001(2015).

    [10] Lin S F, Sheng H X, Li Q W. Handwritten digital classification based on the stacked sparse autoencoders[J]. Microprocessors, 36, 47-51(2015).

         Lin S F, Sheng H X, Li Q W. Handwritten digital classification based on the stacked sparse autoencoders[J]. Microprocessors, 36, 47-51(2015).

    [11] Yu X J, Lu H D, Wu D. Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging[J]. Postharvest Biology and Technology, 141, 39-49(2018). http://www.sciencedirect.com/science/article/pii/S0925521417311912

         Yu X J, Lu H D, Wu D. Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging[J]. Postharvest Biology and Technology, 141, 39-49(2018). http://www.sciencedirect.com/science/article/pii/S0925521417311912

    [12] Qu J L, Du C F, Di Y Z et al[J]. Research and prospect of deep auto-encoders Computer and Modernization, 2014, 128-134.

         Qu J L, Du C F, Di Y Z et al[J]. Research and prospect of deep auto-encoders Computer and Modernization, 2014, 128-134.

    [13] Dai X A, Guo S H, Ren Y et al. Hyperspectral remote sensing image chassification using the stacked sparse autoencoder[J]. Journal of University of Electronic Science and Technology of China, 45, 382-386(2016).

         Dai X A, Guo S H, Ren Y et al. Hyperspectral remote sensing image chassification using the stacked sparse autoencoder[J]. Journal of University of Electronic Science and Technology of China, 45, 382-386(2016).

    [14] Feng Y, Cui N B, Gong D Z et al[J]. Prediction model of reference crop evapotranspiration based on extreme learning machine Transactions of the Chinese Society of Agricultural Engineering, 2015, 153-160.

         Feng Y, Cui N B, Gong D Z et al[J]. Prediction model of reference crop evapotranspiration based on extreme learning machine Transactions of the Chinese Society of Agricultural Engineering, 2015, 153-160.

    [15] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theroy and applications[J]. Neurocomputing, 70, 489-501(2006). http://www.sciencedirect.com/science/article/pii/S0925231206000385

         Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theroy and applications[J]. Neurocomputing, 70, 489-501(2006). http://www.sciencedirect.com/science/article/pii/S0925231206000385

    [16] Vincent P, Larochelle H, Bengio Y et al. Extracting and composing robust features with denoising autoencoders. [C]∥Proceedings of the 25th International Conference on Machine Learning, July 5-9 2008, Helsinki. New York: ACM, 1096-1103(2008).

         Vincent P, Larochelle H, Bengio Y et al. Extracting and composing robust features with denoising autoencoders. [C]∥Proceedings of the 25th International Conference on Machine Learning, July 5-9 2008, Helsinki. New York: ACM, 1096-1103(2008).

    [17] Suktanarak S, Teerachaichayut S. Non-destructive quality assessment of hens’ eggs using hyperspectral images[J]. Journal of Food Engineering, 215, 97-103(2017).

         Suktanarak S, Teerachaichayut S. Non-destructive quality assessment of hens’ eggs using hyperspectral images[J]. Journal of Food Engineering, 215, 97-103(2017).

    [18] Chu X L[M]. Molecular spectroscopy analytical technology combined with chemometrics and its application, 86-88(2011).

         Chu X L[M]. Molecular spectroscopy analytical technology combined with chemometrics and its application, 86-88(2011).

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    Libo Rao, Tao Pang, Ranshi Ji, Xiaoyan Chen, Jie Zhang. Firmness Detection for Apples Based on Hyperspectral Imaging Technology Combined with Stack Autoencoder-Extreme Learning Machine Method[J]. Laser & Optoelectronics Progress, 2019, 56(11): 113001
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