Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks
- 899 Downloads
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%.
KeywordsComputer vision Citrus fruits Decay Non-destructive inspection Hyperspectral imaging ROC curve
This work was partially funded by the Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria de España (INIA) through research project RTA2009-00118-C02-01 and by the Ministerio de Ciencia e Innovación de España (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 València through project UV-INVAE11-41271.
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Eckert, J., & Eaks, I. (1989). Postharvest disorders and diseases of citrus. The citrus industry. Berkeley: University California Press.Google Scholar
- 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.
- Gaffney, J. J. (1973). Reflectance properties of citrus fruits. Transactions of the ASAE, 16(2), 310–314.Google Scholar
- 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.Google Scholar
- 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.CrossRefGoogle Scholar
- Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.
- 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.Google Scholar
- 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.Google Scholar
- 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.
- 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.
- 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.Google Scholar
- 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.Google Scholar
- 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.CrossRefGoogle Scholar
- Rao, C. R., & Mitra, S. K. (1972). Generalized inverse of matrices and its applications. New York: Wiley.Google Scholar
- Rifkin, R., & Klautau, A. (2004). In defense of one-vs-all classification. Journal of Machine Learning Research, 5, 101–141.Google Scholar
- Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107.Google Scholar
- 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.Google Scholar
- Sun, D.-W. (Ed.). (2010). Hyperspectral imaging for food quality analysis and control. London: Academic.Google Scholar
- Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.Google Scholar
- 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.Google Scholar
- 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.Google Scholar