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Spectral Characteristics of Citrus Black Spot Disease

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Spectral Characteristics of Citrus Black Spot Disease Spectral Characteristics of Citrus Black symptoms, but the most distinguish- ing one is called hard spot (Fig. 1), Spot Disease which is a circular lesion (with 3– 10 mm diameter) with gray necrotic 1 1,4 2 fatal tissue at the center embraced by Alireza Pourreza , Won Suk Lee , Mark A. Ritenour , a black margin. Hard spot becomes and Pamela Roberts3 apparent at the time of fruit coloring just before harvesting (Dewdney et al., 2014). Fruit that have CBS ADDITIONAL INDEX WORDS. CBS, fungus, spectrometry, diagnosis, machine lesions are nonvaluable for the fresh learning, band selection, classification markets. Premature fruit drop is also SUMMARY. Citrus black spot (CBS) is a fungal disease caused by Phyllosticta a consequence of CBS disease in citricarpa (synonym Guignardia citricarpa). CBS causes fruit lesions and signifi- severe conditions. There is a long cant yield loss in all citrus (Citrus) species. The most distinguishing CBS symptom is latent stage in the disease cycle in called hard spot, which is a circular lesion with gray tissue at the center surrounded which the CBS symptoms are not by a black margin. The spectral characteristic of CBS lesions was investigated and apparent for several months after in- compared with the spectral signature of healthy fruit tissue to determine the best fection. Symptoms usually appear at distinguishing wave band. Healthy and CBS-affected samples presented similar the time of fruit ripening (Dewdney reflectance below 500 nm and above 900 nm. However, healthy samples reflected more light between 500 and 900 nm, especially within the visible band. Also, et al., 2014). spectral reflectance of the same symptomatic lesion was acquired six times over To reduce the spread of the a 2-month period to determine the variation of symptom’s spectral signatures over disease, CBS must be efficiently con- time after being harvested. A two-sample t test was employed to compare each pair trolled in the grove. In addition, of consecutive repetitions. The results showed that the spectral signature of the CBS manual detection of CBS symptoms lesion did not change significantly over 2 months. The wavelengths between 587 at the packaging process is extremely and 589 nm were identified as the distinguishing band to develop a monochrome difficult because they may be con- vision–based sensor for CBS diagnosis. A support vector machine (SVM) classifier fused with blemishes caused by other was trained using the spectral reflectance data at the selected bands to identify CBS- disorders and the process is time affected samples in each repetition. The overall CBS detection accuracies varied consuming. Therefore, a rapid and between 93.3% and 94.6%. accurate CBS identification technique can expedite the quality control lorida citrus industry has been 2007). Again after a few years, process and help growers for bet- threatened by several, previ- HLB disease was found in all citrus ter disease management. Computer Fously exotic citrus diseases dur- producing counties in Florida vision–based sensors have been widely ing the past few years. Citrus canker and made a huge impact on the used for plant disease identification (caused by Xanthomonas axonopodis $9 billion citrus industry in Florida (Pourreza et al., 2015; Qin et al., pv. citri), a bacterial disease, was (Pourreza et al., 2014). In Mar. 2012; Sankaran et al., 2010). How- discovered in 1995 near the Miami 2010, CBS was diagnosed on fruit ever, using vision sensors for CBS International Airport (Gottwald in some groves near Immokalee, FL identification has not been thor- et al., 2002) and despite all the (Schubert et al., 2013). CBS is oughly investigated. Bulanon et al. efforts to eradicate the disease, it a fungal disease caused by P. citri- (2013) analyzed hyperspectral images became widespread in Florida carpa (Er et al., 2013). The disease of CBS symptoms in the range of mainly because of the 2004–05 was found in Taiwan, South Africa, 480–950 nm (spectral resolution of hurricanes (Stover et al., 2014). and China in 1919, 1920, and 2.8 nm) with the objective of deter- In 2005, another destructive bac- 1936, respectively (Kotze, 1981; mining the potential bands to develop terial disease called Huanglongb- Wang et al., 2012). Later in the a multispectral imaging sensor. They ing (HLB) or citrus greening 1980s, CBS was officially found in defined four wavelengths including caused by Candidatus Liberibacter coastal humid regions of Australia 781, 713, 629, and 493 nm as the asiaticus and vectored by the asian and caused huge yield losses for selected bands for a multispectral im- citrus psyllid (Diaphorina citri) several years (Kotze, 1981). age acquisition system. They achieved was discovered in Florida City, CBS causes fruit lesions and sub- the overall accuracy of 96% using the south of Miami (Gottwald et al., stantial yield loss in all citrus species. four selected wavelengths and nor- Sweet orange (Citrus sinensis)va- malized difference vegetation index We would like to thank the Florida Specialty Crop rieties such as the ‘Valencia’ are band ratio of 781 nm [near-infrared Block Grant Program for funding this research. We extremely susceptible to this dis- (NIR)] and 713 nm (red) as the input would also like to express our sincere appreciation to Ce Yang, Chuanqi Xie, Daeun Choi, and Chuanyuan ease. CBS causes a wide range of features. Zhao for their assistance in this study. 1Department of Agricultural and Biological Engineer- ing, University of Florida, Gainesville, FL 32611 Units 2Indian River Research and Education Center, Uni- To convert U.S. to SI, To convert SI to U.S., versity of Florida, Fort Pierce, FL 34945 multiply by U.S. unit SI unit multiply by 3Southwest Florida Research & Education Center, 25.4 inch(es) mm 0.0394 University of Florida, Immokalee, FL 34142 1 micron(s) mm1 4Corresponding author. E-mail: wslee@ufl.edu. (°F – 32) O 1.8 °F °C(°C · 1.8) + 32 254 • June 2016 26(3) significant difference between the spectral signatures of different rep- etitions. The averages of spectral reflec- tance and their first derivatives were compared between every two consecu- tive repetitions and also between the first and last repetitions for each class. All data analyses were conducted using MATLAB (version R2011a; MathWorks, Natick, MA). Fig. 1. Images of two citrus black spot lesions (CBS positive) and two CBS- BAND SELECTION. To determine negative spots on peels of ‘Valencia’ sweet orange fruit. the most important wavelengths in CBS diagnosis, five feature ranking methods including t test, Kullback– The main goal of this research (QR600-7-SR-125F; Ocean Optics) Leibler distance, Chernoff bound, was to investigate the spectral signa- with a core diameter of 600 mm was receiver operating characteristic, and tures of CBS hard spot lesions using used to measure the spectral reflec- Wilcoxon tests were employed (Liu high spectral resolution data and de- tance of the target spots. A reflection and Motoda, 1998; Theodoridis and termine the best wavelength for CBS probe holder (RPH-1; Ocean Optics) Koutroumbas, 2009). The results of identification. The specific objectives was used to keep the same probe each method included a vector of were to: 1) investigate the progress of positioning to all the target spots coefficients corresponding to the fea- the lesion development on fruit over at 45° in all measurements. The tures (wavelengths). These coefficients time after harvest, 2) select the im- light source (LS-1 Tungsten Halogen represent the level of relevance of each portant wave bands for designing Light Source; Ocean Optics) of the wavelength for the classification pro- a customized and image acquisition spectrometer was turned on 45 min cess. The coefficient ranges for each system to CBS symptoms, and 3) before measurement to ensure it method varied since different factors evaluate the classification accuracy reached its stable status. The optical were considered for different feature using the selected band as the input reference standard was collected be- ranking methods. Therefore, the co- feature. fore each measurement using a certi- efficients in each method were nor- fied 99% white reflectance standard malized by the maximum obtained Materials and methods (SRS-99-020; Labsphere, North Sut- coefficient that belonged to the top DATA COLLECTION. Healthy and ton, NH). wavelength ranked with the corre- CBS symptomatic citrus fruit of The measurement was repeated sponding method. To select the most ‘Valencia’ sweet orange were col- six times on 11 Apr., 18 Apr., 2 May, relevant bands, a combination of the lected from a citrus grove near Immo- 15 May, 29 May, and 13 June 2014. entire feature ranking results was used kalee, FL, in Apr. 2014. To conduct The CBS-negative samples were added to appoint the best set of nominated the spectral measurement, the citrus to the dataset from the second repeti- wavelengths, which were voted by all samples were transferred to the post- tion (18 Apr.). At the forth repetition methods. For this purpose, all the harvest laboratory in the Indian River of spectral measurement, a few sam- wavelengths that had the coefficients Research and Education Center, Uni- ples began to decay. These decayed greater than 0.99 in all methods were versity of Florida, Fort Pierce. A total samples were removed before the fifth selected. of 134 citrus fruit including 104 and sixth repetitions of spectral mea- CLASSIFICATION. The selected CBS-positive and 30 CBS-negative surement. Color images of the citrus bands were used to train a SVM bi- samples were selected randomly for samples and the selected spots were nary classifier with two classes of CBS spectral measurement. Two or three taken as a reference of the sample positive and CBS negative (Bishop, CBS symptomatic hard spot lesions status at the time of the spectral mea- 2006).
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