- -

Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Selection of optimal ...
Tamaño: 253.5Kb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: D. Lorente;Aleixo ...
Tamaño: 450.7Kb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Lorente, Delia es_ES
dc.contributor.author Aleixos Borrás, María Nuria es_ES
dc.contributor.author Gómez Sanchís, Juan es_ES
dc.contributor.author Cubero, Sergio es_ES
dc.contributor.author Blasco Ivars, José es_ES
dc.date.accessioned 2016-07-22T10:00:23Z
dc.date.available 2016-07-22T10:00:23Z
dc.date.issued 2013-02
dc.identifier.issn 1935-5130
dc.identifier.uri http://hdl.handle.net/10251/68023
dc.description.abstract Early automatic detection of fungal infections in post-harvest citrus fruits is especially important for the citrus industry because only a few infected fruits can spread the infection to a whole batch during operations such as storage or exportation, thus causing great economic losses. Nowadays, this detection is carried out manually by trained workers illuminating the fruit with dangerous ultraviolet lighting. The use of hyperspectral imaging systems makes it possible to advance in the development of systems capable of carrying out this detection process automatically. However, these systems present the disadvantage of generating a huge amount of data, which must be selected in order to achieve a result that is useful to the sector. This work proposes a methodology to select features in multi-class classification problems using the receiver operating characteristic curve, in order to detect rottenness in citrus fruits by means of hyperspectral images. The classifier used is a multilayer perceptron, trained with a new learning algorithm called extreme learning machine. The results are obtained using images of mandarins with the pixels labelled in five different classes: two kinds of sound skin, two kinds of decay and scars. This method yields a reduced set of features and a classification success rate of around 89%. es_ES
dc.description.sponsorship This work was partially funded by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria de Espana (INIA) through research project RTA2009-00118-C02-01 and by the Ministerio de Ciencia e Innovacion de Espana (MICINN) through research project DPI2010-19457, both projects with the support of European FEDER funds. This work was also been partially funded by Universitat de Valencia through project UV-INVAE11-41271. en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag es_ES
dc.relation.ispartof Food and Bioprocess Technology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Computer vision es_ES
dc.subject Citrus fruits es_ES
dc.subject Decay es_ES
dc.subject Non-destructive inspection es_ES
dc.subject Hyperspectral imaging es_ES
dc.subject ROC curve es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.title Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11947-011-0737-x
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//RTA2009-00118-C02-01/ES/RTA2009-00118-C02-01/ es_ES
dc.relation.projectID 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/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UV//UV-INV-AE11-41271/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica es_ES
dc.contributor.affiliation 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 es_ES
dc.description.bibliographicCitation Lorente, D.; Aleixos Borrás, MN.; Gómez Sanchís, J.; Cubero, S.; Blasco Ivars, J. (2013). Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks. Food and Bioprocess Technology. 6(2):530-541. https://doi.org/10.1007/s11947-011-0737-x es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/s11947-011-0737-x es_ES
dc.description.upvformatpinicio 530 es_ES
dc.description.upvformatpfin 541 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 212466 es_ES
dc.contributor.funder Universitat de València es_ES
dc.description.references Aleixos, N., Blasco, J., Navarrón, F., & Moltó, E. (2002). Multispectral inspection of citrus in real time using machine vision and digital signal processors. Computers and Electronics in Agriculture, 33(2), 121–137. es_ES
dc.description.references Ariana, D. P., Guyer, D. E., & Shrestha, B. (2006). Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Computers and Electronics in Agriculture, 50, 148–161. es_ES
dc.description.references Balasundaram, D., Burks, T. F., Bulanona, D. M., Schubert, T., & Lee, W. S. (2009). Spectral reflectance characteristics of citrus canker and other peel conditions of grapefruit. Postharvest Biology and Technology, 51, 220–226. es_ES
dc.description.references Bennedsen, B. S., Peterson, D. L., & Tabb, A. (2007). Identifying apple surface defects using principal components analysis and artificial neural networks. Transactions of the ASABE, 50(6), 2257–2265. es_ES
dc.description.references Blanc, P. G. R., Blasco, J., Moltó, E., Gómez-Sanchis, J., Cubero. S. (2009). System for the automatic selective separation of rotten citrus fruit. European patent EP2133157A1. es_ES
dc.description.references Blanc, P. G. R., Blasco, J., Moltó, E., Gómez-Sanchis, J., Cubero, S. (2010). System for the automatic selective separation of rotten citrus fruit. United States patent US2010/0121484A1. es_ES
dc.description.references 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. es_ES
dc.description.references Blasco, J., Aleixos, N., Gómez-Sanchis, J., & Moltó, E. (2009). Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103, 137–145. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references Du, C.-J., & Sun, D.-W. (2009). Retrospective shading correlation of confocal laser scanning microscopy beef images for three-dimensional visualization. Food and Bioprocess Technology, 2, 167–176. es_ES
dc.description.references Eckert, J., & Eaks, I. (1989). Postharvest disorders and diseases of citrus. The citrus industry. Berkeley: University California Press. es_ES
dc.description.references Farrera-Rebollo, R. R., Salgado-Cruz, M. P., Chanona-Pérez, J., Gutiérrez-López, G. F., Alamilla-Beltrán, L., & Calderón-Domínguez, G. (2011). Evaluation of image analysis tools for characterization of sweet bread crumb structure. Food and Bioprocess Technology. doi: 10.1007/s11947-011-0513-y . es_ES
dc.description.references Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. es_ES
dc.description.references Gaffney, J. J. (1973). Reflectance properties of citrus fruits. Transactions of the ASAE, 16(2), 310–314. es_ES
dc.description.references Gitelson, A., Merzyak, M. N., & Lichtenthaler, H. K. (1996). Detection of red-edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology, 148, 501–508. es_ES
dc.description.references Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., & Blasco, J. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86. es_ES
dc.description.references Gómez-Sanchis, J., Moltó, E., Camps-Valls, G., Gómez-Chova, L., Aleixos, N., & Blasco, J. (2008). Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. Journal of Food Engineering, 85(2), 191–200. es_ES
dc.description.references 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. es_ES
dc.description.references Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182. es_ES
dc.description.references Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, 416–426. es_ES
dc.description.references Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70, 489–501. es_ES
dc.description.references Huang, Y., Kangas, L. J., & Rasco, B. A. (2007). Applications of artificial neural networks (ANNs) in food science. Critical Reviews in Food Science and Nutrition, 47(2), 113–126. es_ES
dc.description.references Jiménez-Cuesta, M., Cuquerella, J., & Martínez-Jávega, J. M. (1981). Determination of a color index for citrus fruit degreening. In: Proceedings of the International Society of Citriculture, 2, 750–753. es_ES
dc.description.references Karimi, Y., Maftoonazad, N., Ramaswamy, H. S., Prasher, S. O., & Marcotte, M. (2009). Application of hyperspectral technique for color classification avocados subjected to different treatments. Food and Bioprocess Technology. doi: 10.1007/s11947-009-0292-x . es_ES
dc.description.references Kim, D. G., Burks, T. F., Qin, J., & Bulanon, D. M. (2009). Classification of grapefruit peel diseases using color texture feature analysis. International Journal of Agricultural and Biological Engineering, 2(3), 41–50. es_ES
dc.description.references 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. es_ES
dc.description.references Kurita, M., Kondo, N., Shimizu, H., Ling, P., Falzea, P. D., Shiigi, T., Ninomiya, K., Nishizu, T., & Yamamoto, K. (2009). A double image acquisition system with visible and UV LEDs for citrus fruit. Journal of Robotics and Mechatronics, 21(4), 533–540. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2011). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology. doi: 10.1007/s11947-011-0725-1 . es_ES
dc.description.references Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & Nicolaï, B. (2011). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology.. doi: 10.1007/s11947-011-0697-1 . es_ES
dc.description.references Manickavasagan, A., Jayas, D. S., White, N. D. G., & Paliwal, J. (2010). Wheat class identification using thermal imaging. Food and Bioprocess Technology, 3(3), 450–460. es_ES
dc.description.references Naidu, R. A., Perry, E. M., Pierce, F. J., & Mekuria, T. (2009). The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two redberried wine grape cultivars. Computers and Electronics in Agriculture, 66, 38–45. es_ES
dc.description.references Obagwu, J., & Korsten, L. (2003). Integrated control of citrus green and blue molds using Bacillus subtilis in combination with sodium bicarbonate or hot water. Postharvest Biology and Technology, 28(1), 187–194. es_ES
dc.description.references Obenland, D., Margosan, D., Collins, S., Sievert, J., Fjeld, K., Arpaia, M. L., Thompson, J., & Slaughter, D. (2009). Peel fluorescence as a means to identify freeze-damaged navel oranges. HortTechnology, 19(2), 379–384. es_ES
dc.description.references 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. es_ES
dc.description.references Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., Marconcini, M., Tilton, J. C., & Trianni, G. (2009). Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113(1), S110–S122. es_ES
dc.description.references Prechelt, L. (1996). A quantitative study of experimental evaluations of neural network learning algorithms: Current research practice. Neural Networks, 9(3), 457–462. es_ES
dc.description.references 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. es_ES
dc.description.references Quevedo, R., & Aguilera. (2010). Color computer vision and stereoscopy for estimating firmness in the salmon (Salmon salar) fillets. Food and Bioprocess Technology, 3(4), 561–567. es_ES
dc.description.references Quevedo, R., Aguilera, J. M., & Pedreschi, F. (2010). Color of salmon fillets by computer vision and sensory panel. Food and Bioprocess Technology, 3(5), 637–643. es_ES
dc.description.references Rao, C. R., & Mitra, S. K. (1972). Generalized inverse of matrices and its applications. New York: Wiley. es_ES
dc.description.references Rifkin, R., & Klautau, A. (2004). In defense of one-vs-all classification. Journal of Machine Learning Research, 5, 101–141. es_ES
dc.description.references Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107. es_ES
dc.description.references Serrano AJ, Soria E, Martín JD, 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. es_ES
dc.description.references Shih, F. Y. (2010). Image processing and pattern recognition: Fundamentals and techniques. New York: Wiley-IEEE. es_ES
dc.description.references 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. es_ES
dc.description.references Sun, D.-W. (Ed.). (2010). Hyperspectral imaging for food quality analysis and control. London: Academic. es_ES
dc.description.references Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. es_ES
dc.description.references Unay, D., & Gosselin, B. (2006). Automatic defect segmentation of ‘Jonagold’ apples on multi-spectral images: A comparative study. Postharvest Biology and Technology, 42, 271–279. es_ES
dc.description.references Xu, H. R., Ying, Y. B., Fu, X. P., & Zhu, S. P. (2007). Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf. Biosystems Engineering, 96(4), 447–454. es_ES
dc.description.references Yang, C. M., Cheng, C. H., & Chen, R. K. (2007). Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Science, 47, 329–335. es_ES


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

Mostrar el registro sencillo del ítem