- -

Comparison of ROC Feature Selection Method for the Detection of Decay in Citrus Fruit Using Hyperspectral Images

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Comparison of ROC Feature Selection Method for the Detection of Decay in Citrus Fruit Using Hyperspectral Images

Mostrar el registro completo del ítem

Lorente, D.; Blasco Ivars, J.; Serrano López, AJ.; Soria Olivas, E.; Aleixos Borrás, MN.; Gómez Sanchís, J. (2013). Comparison of ROC Feature Selection Method for the Detection of Decay in Citrus Fruit Using Hyperspectral Images. Food and Bioprocess Technology. 6(12):3613-3619. https://doi.org/10.1007/s11947-012-0951-1

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/77408

Ficheros en el ítem

Thumbnail
Nombre: Manuscript-Senia.pdf
Tamaño: 207.0Kb
Formato: PDF
Descripción: Versión del Autor.
Abrir/Preview
[Cerrado]
Nombre: D. Lorente;BLASCO ...
Tamaño: 1002.Kb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor

Metadatos del ítem

Título: Comparison of ROC Feature Selection Method for the Detection of Decay in Citrus Fruit Using Hyperspectral Images
Autor: Lorente, D. Blasco Ivars, José Serrano López, Antonio José Soria Olivas, Emilio Aleixos Borrás, María Nuria Gómez Sanchís, Juan
Entidad UPV: Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà
Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària
Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica
Fecha difusión:
Resumen:
Hyperspectral imaging systems allow to detect the initial stages of decay caused by fungi in citrus fruit automatically, instead of doing it manually under dangerous ultraviolet illumination, thus preventing the fungal ...[+]
Palabras clave: Citrus fruit , Computer vision , Decay , Feature selection , Hyperspectral imaging , Non-destructive inspection , ROC curve
Derechos de uso: Reserva de todos los derechos
Fuente:
Food and Bioprocess Technology. (issn: 1935-5130 ) (eissn: 1935-5149 )
DOI: 10.1007/s11947-012-0951-1
Editorial:
Springer Verlag
Versión del editor: http://dx.doi.org/10.1007/s11947-012-0951-1
Código del Proyecto:
info:eu-repo/grantAgreement/UV//UV-INV-AE11-41271/
info:eu-repo/grantAgreement/MICINN//RTA2009-00118-C02-01/ES/RTA2009-00118-C02-01/
info:eu-repo/grantAgreement/MICINN//DPI2010-19457/ES/DESARROLLO DE NUEVAS TECNICAS DE VISION POR COMPUTADOR BASADAS EN SISTEMAS MULTI-AGENTE E IMAGENES HIPERESPECTRALES PARA LA ESTIMACION AUTOMATICA DE LA CALIDAD DE LOS CITRICOS/
Agradecimientos:
This work has been partially funded by the Universitat de València through project UV-INV-AE11-41271, by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria de España (INIA) through research project ...[+]
Tipo: Artículo

References

Abe, N., Kudo, M., Toyama, J., & Shimbo, M. (2000). A divergence criterion for classifier independent feature selection. Lecture notes in computer science. Advances in Pattern Recognition, 1876, 668–676.

Blasco, J., Aleixos, N., Gómez, J., & Moltó, E. (2007). Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering, 83(3), 384–393.

Bonnlander, B.V., & Weigend, A.S. (1994). Selecting input variables using mutual information and nonparametric density estimation. In: Proceedings of the 1994 International Symposium on Artificial Neural Networks (ISANN’94), Tainan, Taiwan, pp. 42–50. [+]
Abe, N., Kudo, M., Toyama, J., & Shimbo, M. (2000). A divergence criterion for classifier independent feature selection. Lecture notes in computer science. Advances in Pattern Recognition, 1876, 668–676.

Blasco, J., Aleixos, N., Gómez, J., & Moltó, E. (2007). Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering, 83(3), 384–393.

Bonnlander, B.V., & Weigend, A.S. (1994). Selecting input variables using mutual information and nonparametric density estimation. In: Proceedings of the 1994 International Symposium on Artificial Neural Networks (ISANN’94), Tainan, Taiwan, pp. 42–50.

Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.

Choi, E., & Lee, C. (2003). Feature extraction based on the Bhattacharyya distance. Pattern Recognition, 36(8), 1703–1709.

Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487–504.

Eckert, J., & Eaks, I. (1989). Postharvest disorders and diseases of citrus. In W. Reuther, E. C. Calavan, & G. E. Carman (Eds.), The citrus industry. Berkeley: University California Press.

ElMasry, G., Wang, N., Vigneault, C., Qiao, J., & ElSayed, A. (2008). Early detection of apple bruises on different background colors using hyperspectral imaging. LWT, 41, 337–345.

ElMasry, G., Wang, N., & Vigneault, C. (2009). Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks. Postharvest Biology and Technology, 52, 1–8.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.

Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., et al. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86.

Gómez-Sanchis, J., Martín-Guerrero, J. D., Soria-Olivas, E., Martínez-Sober, M., Magdalena-Benedito, R., & Blasco, J. (2012). Detecting rottenness caused by Penicillium in citrus fruits using machine learning techniques. Expert Systems with Applications, 39(1), 780–785.

Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70, 489–501.

Kondo, N., Ahmad, U., Monta, M., & Murase, H. (2000). Machine vision based quality evaluation of Iyokan orange fruit using neural networks. Computers and Electronics in Agriculture, 29, 135–147.

Kullback, S. (1987). The Kullback–Leibler distance. The American Statistician, 41, 340–341.

Kurita, M., Kondo, N., Shimizu, H., Ling, P., Falzea, P. D., Shiigi, T., et al. (2009). A double image acquisition system with visible and UV LEDs for citrus fruit. Journal of Robotics and Mechatronics, 21(4), 533–540.

Li, S., Liao, C., & Kwok, J. (2006). Gene feature extraction using T-test statistics and Kernel partial least squares. Lecture notes in computer science. Neural Information and Processing, 4234, 11–20.

Li, J., Rao, X., & Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78(1), 38–48.

Liu, Y., Chen, Y. R., Wang, C. Y., Chan, D. E., & Kim, M. S. (2005). Development of a simple algorithm for the detection of chilling injury in cucumbers from visible/near-infrared hyperspectral imaging. Applied Spectroscopy, 59(1), 78–85.

López-García, F., Andreu-García, A., Blasco, J., Aleixos, N., & Valiente, J. M. (2010). Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, 71, 189–197.

Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., & Blasco, J. (2011). Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food and Bioprocess Technology. doi: 10.1007/s11947-011-0737-x .

Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142.

Ouardighi, A., Akadi, A., Aboutajdine, D. (2007). Feature selection on supervised classification using Wilks lambda statistic. In: International Symposium on Computational Intelligence and Intelligent Informatics ISCIII07, 1, pp. 51–55.

Palou, L., Smilanik, J., Usall, J., & Viñas, I. (2001). Control postharvest blue and green molds of oranges by hot water, sodium carbonate, and sodium bicarbonate. Plant Disease, 85, 371–376.

Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238.

Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps-Valls, G., et al. (2009). Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113(1), S110–S122.

Ponsa, D., & López, A. (2007). Feature selection based on a new formulation of the minimal-redundancy-maximal-relevance criterion. Lecture notes in computer science. Pattern Recognition and Image Analysis, 4477, 47–54.

Qin, J., Burks, T. F., Ritenour, M. A., & Bonn, W. G. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 93, 183–191.

Qin, J., Burks, T. F., Zhao, X., Niphadkar, N., & Ritenour, M. A. (2012). Development of a two-band spectral imaging system for real-time citrus canker detection. Journal of Food Engineering, 108(1), 87–93.

Rifkin, R., & Klautau, A. (2004). In defense of one-vs-all classification. Journal of Machine Learning Research, 5, 101–141.

Rodgers, J. L., & Nicewander, A. W. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1), 59–66.

Serrano, A.J., Soria, E., Martín, J.D., Magdalena, R. Gómez, J. (2010). Feature selection using ROC curves on classification problems. In: International Joint Conference on Neural Networks, IJCNN 2010. 28th–30th July 2010. Barcelona, Spain. Proceedings, pp. 1980–1985.

Shih, F. Y. (2010). Image processing and pattern recognition: fundamentals and techniques. New York: Wiley-IEEE.

Slaughter, D. C., Obenland, D. M., Thompson, J. F., Arpaia, M. L., & Margosan, D. A. (2008). Non-destructive freeze damage detection in oranges using machine vision and ultraviolet fluorescence. Postharvest Biology and Technology, 48, 341–346.

Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (4th ed.). New York: Springer.

Xing, J., Bravo, C., Jancsók, P. T., Ramon, H., & De Baerdemaeker, J. (2005). Detecting bruises on ‘Golden Delicious’ apples using hyperspectral imaging with multiple wavebands. Biosystems Engineering, 90(1), 27–36.

Zude, M. (Ed.). (2008). Optical monitoring of fresh and processed agricultural crops. Boca Raton: CRC.

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem