Nondestructive Detecting Rottenness Defect of Citrus By Using Hyper-Spectra Imaging Technology
CHU Bing-quan1, ZHANG Hai-liang1,2, LUO Wei2, HE Yong1*
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2. East China Jiaotong University, Nanchang 330013, China
Abstract：Rottenness is a prevalent and devastating disease that threats citrus fruit. Automatic detection of rottenness can enhance the competitiveness and profitability of the citrus industry. In this study, hyper-spectral image technology was used nondestructively to detect citrus rottenness. Spectral curve in defects peel region of interest was analyzed and combined with principal component analysis to extract the four best bands. Principal component was used based on four best bands: 615 nm and 680 nm, 710 nm and 725 nm peaks combination respectively and ultimately selected component (PC-2) as image classification and recognition obtained from the 615 nm and 680 nm principal component analysis and identification rate was 100% with a simple threshold segmentation. These results showed that using hyper-spectral as a kind of detection methods could be used for the evaluation of citrus rotteness recognition.
Key words：Hyper-spectral imaging; Citrus; Rottenness defect; Principal component analysis
[1] HE Yong, LIU Fei, LI Xiao-li, et al(何 勇，刘 飞，李晓丽，等). Spectroscopy and Imaging Technolog in Agriculture(光谱及成像技术在农业中的应用). Beijing: Science Press(北京： 科学出版社)，2015. 6.
[2] Li J, Huang W, Tian X, et al. Computers and Electronics in Agriculture, 2016, 127: 582.
[3] Shi J Y, Hu X T, Zou X B, et al. Food Chemistry, 2017, 229(15): 235.
[4] Kamruzzaman M, Makino Y, Oshita S. Journal of Food Engineering, 2016, 170: 8.
[5] Kumar A, Lee W S, Ehsani R J, et al. Journal of Applied Remote Sensing, 2012, 6(1): 063542.
[6] ElMasry G, Wang N, Vigneault C, et al. LWT-Food Science and Technology, 2008, 41(2): 337.
[7] Ariana D P, Lu R F, Guyer D E. Computers and Electronics in Agriculture, 2006, 53(1): 60.
[8] Gómez-Sanchis J, Gómez-Chova L, Aleixos N, et al. Journal of Food Engineering, 2008, 89(1): 80.
[9] CHEN Xin-xin, GUO Chen-tong, ZHANG Chu, et al(陈欣欣，郭辰彤，张 初，等). Spectroscoy and Spectral Analysis(光谱学与光谱分析), 2017, 37(1): 150.
[10] GUO En-you，LIU Mu-hua，ZHAO Jie-wen, et al(郭恩有，刘木华，赵杰文，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2008，(5): 91.
[11] CAI Jian-rong，WANG Jian-hei，CHEN Quan-sheng, et al(蔡健荣，王建黑，陈全胜，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2009，(1): 127.
[12] ZHAO Jie-wen，LIU Jian-hua，CHEN Quan-sheng, et al(赵杰文，刘剑华，陈全胜，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2008，(1): 106.
