Florida citrus industry has been threatened by several, previously exotic citrus diseases during the past few years. Citrus canker (caused by Xanthomonas axonopodis pv. citri), a bacterial disease, was discovered in 1995 near the Miami International Airport (Gottwald et al., 2002) and despite all the efforts to eradicate the disease, it became widespread in Florida mainly because of the 2004–05 hurricanes (Stover et al., 2014). In 2005, another destructive bacterial disease called Huanglongbing (HLB) or citrus greening caused by Candidatus Liberibacter asiaticus and vectored by the asian citrus psyllid (Diaphorina citri) was discovered in Florida City, south of Miami (Gottwald et al., 2007). Again after a few years, HLB disease was found in all citrus producing counties in Florida and made a huge impact on the $9 billion citrus industry in Florida (Pourreza et al., 2014). In Mar. 2010, CBS was diagnosed on fruit in some groves near Immokalee, FL (Schubert et al., 2013). CBS is a fungal disease caused by P. citricarpa (Er et al., 2013). The disease was found in Taiwan, South Africa, and China in 1919, 1920, and 1936, respectively (Kotzé, 1981; Wang et al., 2012). Later in the 1980s, CBS was officially found in coastal humid regions of Australia and caused huge yield losses for several years (Kotzé, 1981).
CBS causes fruit lesions and substantial yield loss in all citrus species. Sweet orange (Citrus sinensis) varieties such as the ‘Valencia’ are extremely susceptible to this disease. CBS causes a wide range of symptoms, but the most distinguishing one is called hard spot (Fig. 1), which is a circular lesion (with 3–10 mm diameter) with gray necrotic fatal tissue at the center embraced by a black margin. Hard spot becomes apparent at the time of fruit coloring just before harvesting (Dewdney et al., 2014). Fruit that have CBS lesions are nonvaluable for the fresh markets. Premature fruit drop is also a consequence of CBS disease in severe conditions. There is a long latent stage in the disease cycle in which the CBS symptoms are not apparent for several months after infection. Symptoms usually appear at the time of fruit ripening (Dewdney et al., 2014).
To reduce the spread of the disease, CBS must be efficiently controlled in the grove. In addition, manual detection of CBS symptoms at the packaging process is extremely difficult because they may be confused with blemishes caused by other disorders and the process is time consuming. Therefore, a rapid and accurate CBS identification technique can expedite the quality control process and help growers for better disease management. Computer vision–based sensors have been widely used for plant disease identification (Pourreza et al., 2015; Qin et al., 2012; Sankaran et al., 2010). However, using vision sensors for CBS identification has not been thoroughly investigated. Bulanon et al. (2013) analyzed hyperspectral images of CBS symptoms in the range of 480–950 nm (spectral resolution of 2.8 nm) with the objective of determining the potential bands to develop a multispectral imaging sensor. They defined four wavelengths including 781, 713, 629, and 493 nm as the selected bands for a multispectral image acquisition system. They achieved the overall accuracy of 96% using the four selected wavelengths and normalized difference vegetation index band ratio of 781 nm [near-infrared (NIR)] and 713 nm (red) as the input features.
The main goal of this research was to investigate the spectral signatures of CBS hard spot lesions using high spectral resolution data and determine the best wavelength for CBS identification. The specific objectives were to: 1) investigate the progress of the lesion development on fruit over time after harvest, 2) select the important wave bands for designing a customized and image acquisition system to CBS symptoms, and 3) evaluate the classification accuracy using the selected band as the input feature.
Bishop, C.M. 2006 Pattern recognition and machine learning. 1st ed. Springer, New York, NY
Bulanon, D.M., Burks, T.F., Kim, D.G. & Ritenour, M.A. 2013 Citrus black spot detection using hyperspectral image analysis Agr. Eng. Intl. CIGR J. 15 171 180
Dewdney, M.M., Schubert, T.S., Estes, M.R. & Peres, N.A. 2014 Florida citrus pest management guide: Citrus black spot. 10 Aug. 2015. <http://edis.ifas.ufl.edu/CG088>
Er, H., Roberts, P., Marois, J. & van Bruggen, A. 2013 Potential distribution of citrus black spot in the United States based on climatic conditions Eur. J. Plant Pathol. 137 635 647
Gottwald, T.R., da Graça, J.V. & Bassanezi, R.B. 2007 Citrus Huanglongbing: The pathogen and its impact Plant Health Prog. doi:10.1094/PHP-2007-0906-01-RV
Gottwald, T.R., Graham, J.H. & Schubert, T.S. 2002 Citrus canker: The pathogen and its impact Plant Health Prog. doi:10.1094/PHP-2002-0812-01-RV
Liu, H. & Motoda, H. 1998 Feature selection for knowledge discovery and data mining. 1st ed. Springer, Berlin, Germany
Pourreza, A., Lee, W.S., Ehsani, R., Schueller, J.K. & Raveh, E. 2015 An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor Comput. Electron. Agr. 110 221 232
Pourreza, A., Lee, W.S., Raveh, E., Ehsani, R. & Etxeberria, E. 2014 Citrus Huanglongbing detection using narrow-band imaging and polarized illumination Trans. Amer. Soc. Agr. Biol. Eng. 57 259 272
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 J. Food Eng. 108 87 93
Sankaran, S., Mishra, A., Ehsani, R. & Davis, C. 2010 A review of advanced techniques for detecting plant diseases Comput. Electron. Agr. 72 1 13
Schubert, T., Dewdney, M., Peres, N., Palm, M., Jeyaprakash, A., Sutton, B., Mondal, S., Wang, N.-Y., Rascoe, J. & Picton, D. 2013 First report of Guignardia citricarpa associated with citrus black spot on sweet orange (Citrus sinensis) in North America Eur. J. Plant Pathol. 137 635 647
Snedecor, G.W. & Cochran, W.G. 1989 Statistical methods. 8th ed. Iowa State Univ. Press, Ames, IA
Stover, E., Driggers, R., Richardson, M.L., Hall, D.G., Duan, Y. & Lee, R.F. 2014 Incidence and severity of asiatic citrus canker on diverse citrus and citrus-related germplasm in a Florida field planting HortScience 49 4 9
Theodoridis, S. & Koutroumbas, K. 2009 Pattern recognition. 4th ed. Elsevier, Boston, MA
Wang, X., Chen, G., Huang, F., Zhang, J., Hyde, K.D. & Li, H. 2012 Phyllosticta species associated with citrus diseases in China Fungal Divers. 52 209 224