Estimation of citrus canker lesion size using hyperspectral reflectance imaging
Keywords:
citrus canker, lesion size, disease detection, hyperspectral reflectance imaging, image classification, multispectral algorithm, size detection limitAbstract
The Citrus industry has need for effective approaches to remove fruit with canker before they are shipped to selective international market such as the European Union. This research aims to determine the detectable size limit for cankerous lesions using hyperspectral imaging approaches. Previously developed multispectral algorithms using visible to near-infrared wavelengths, were used to segregate cankerous citrus fruits from other peel conditions (normal, greasy spot, insect damage, melanose, scab and wind scar). However, this previous work did not consider lesion size. A two-band ratio method with a simple threshold based classifier (ratio of reflectance at wavelengths 834 nm and 729 nm), which gave maximum overall classification accuracy of 95.7%, was selected for lesion size estimation in this study. The smallest size of cankerous lesion detected in terms of equivalent diameter was 1.66 mm. The effect of variation of threshold values and number of erosion cycles (applying morphological erosion multiple times to the image) on estimation of smallest detectable lesion was observed. It was found that small threshold values gave better canker classification accuracies, while exhibiting a lower overall classification accuracy. Meanwhile, higher threshold values portrayed the opposite tendency. The threshold value of 1.275 gave the optimum tradeoff between canker classification accuracy, overall classification accuracy and minimal lesion size detection. Increasing the number of erosion cycles reduced detection rates of smaller canker lesions, leading to the conclusion that a single erosion cycle gave the best size estimation results. The erosion kernel of the size 3 mmReferences
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[2] Qin J W, Burks T F, Ritenour M A, Bonn W G. Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 2009; 93(2): 183-191.
[3] Qin J W, Burks T F, Kim M S, Chao K, Ritenour M A. Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sensing and Instrumentation for Food Quality and Safety, 2008; 2(3): 168-177.
[4] USDA APHIS. Citrus Canker: Movement of Fruit from Quarantined Areas. Federal Register: Rules and Regulations, 2009; 74(203): 54431-54445.
[5] Miller W M, Drouillard G P. Multiple feature analysis for machine vision grading of Florida citrus. Applied Engineering in Agriculture, 2001; 17(5): 627-633.
[6] Pydipati R, Burks T F, Lee W S. Identification of citrus disease using color texture features and discriminant analysis. Computers and Electronics in Agriculture, 2006; 52(1-2): 49-59.
[7] Blasco J, Aleixos N, Gomez J, Molto E. Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering. 2007; 83(3): 384-393.
[8] Gomez-Sanchis J, Molto E, Camps-Valls G, Gomez-Chova L, Aleixos N, Blasco J. 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, 2008; 85(2): 191-200.
[9] Qin J W, Burks T F, Zhao X H, Niphadkar N, Ritenour M A. Hyperspectral band selection for multispectral detection of citrus canker. Transactions of ASABE, 2011; 54(6): 1-11.
[10] Kim M S, Chen Y R, Mehl P M. Hyperspectral reflectance and fluorescence imaging system for food quality and safety. 2001, ASAE ISSN 0001-2351.
[11] Kim M S, Lefcourt A M, Chao K, Chen Y R, Kim I, Chan D E. Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part I. Application of visible and near-infrared reflectance imaging. Transactions of ASAE, 2002; 45(6): 2027- 2037.
[12] Qin J W, Burks T F, Zhao X H, Niphadkar N, Ritenour M A. Development of a two-band spectral imaging system for real-time citrus canker detection. Journal of Food Engineering, 2012, 108(1): 87-93.
[13] Timmer L W, Garnsey S M, Graham J H. Compendium of Citrus Diseases (Second Edition). 2000, the American Phytopathological Society, St. Paul, MN, USA.
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Published
2013-09-22
How to Cite
Niphadkar, N. P., Burks, T. F., Qin, J., & Ritenour, M. A. (2013). Estimation of citrus canker lesion size using hyperspectral reflectance imaging. International Journal of Agricultural and Biological Engineering, 6(3), 41–51. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/679
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Information Technology, Sensors and Control Systems
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