Visual Detection Study on Early Bruises of Korla Pear Based on Hyperspectral Imaging Technology
CHEN Xin-xin1, GUO Chen-tong2, ZHANG Chu1, LIU Zi-yi1, JIANG Hao1, LOU Bing-gan2, HE Yong1*
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China 2. College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
Abstract:In this paper, hyperspectral imaging combined with chemometrics was applied for the detection of internal defects of Korla pear. The hyperspectral images covering the spectral range of 380~1 030 nm were acquired for 60 Korla pears before, and seven consecutive days after internal damages were induced by being dropped from a distance of 30 cm. The mean spectrum were computed from region of interests (ROI) of pear in each image, and was preprocessed with wavelet transform for eliminating system noise and external disturbances, and optimizing the spectral identification region (470~963 nm). Based on the preprocessed samples, the support vector machine models were built respectively through the full and feature wavebands selected by the second derivative. The results on testing set demonstrate that both of the two approaches achieved the discrimination accuracy of 93.75%. Furthermore, F-value based method was applied for image analysis to find out the optimal waveband ratio for the visual discrimination of bruises against normal surface. Based on the optimal waveband ratio images, the selective search algorithm was utilized for segmenting bruises from the pear surface, and shows the accurate identification results. Our research revealed that the hyperspectral imaging technique for detecting bruised features in pears is feasible, which could provide a theoretical reference and basis for designing classification system of fruits in further work.
Key words:Hyperspectral imaging;Bruised detection;SVM;Band ratio math;Korla pear
[1] LAN Hai-peng, JIA Fu-guo, TANG Yu-rong, et al(兰海鹏, 贾富国, 唐玉荣, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31 (5): 325. [2] Vetrekar N, Gad R S, Fernandes I, et al. Journal of Food Science and Technology-Mysore, 2015, 52(11): 6978. [3] He H J, Sun D W. Trends in Food Science & Technology, 2015, 9: 99. [4] Zhang B H, Li J B, Fan S X, et al. Computers and Electronics in Agriculture, 2015, 114: 14. [5] HUANG Wen-qian, CHEN Li-ping, LI Jiang-bo, et al(黄文倩, 陈立平, 李江波, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(1): 272. [6] Liu Y L, Chen Y R, Kim M S, et al. Journal of Food Engineering, 2007, 27: 412. [7] Yu K Q, Zhao Y R, Liu Z Y, et al. Food and Bioprocess Technology, 2014, 7(11): 3077. [8] Li X L, Nie P C, Qiu Z J, et al. Expert Systems With Applications, 2011, 38(9): 11149. [9] Barbin D, Elmasry G, Sun D W, et al. Meat Science, 2012, 90: 259. [10] Wu D, Yang H Q, Chen X J, et al. Journal of Food Engineering, 2008, 88(4): 474. [11] Li J B, Rao X Q, Ying Y B. Computer Electronic Agriculture, 2011, 78(1): 38. [12] 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, 25(1): 127. [13] Uijlings J R R, van de Sande K E A, Gevers T, et al. International Journal of Computer Vision, 2013, 104(2): 154. [14] Chen D, Gran E. Analytical and Bioanalytical Chemistry, 2011, 11: 625. [15] Abbott J A, Lu R, Upchurch B L, et al. Horticultural Reviews, 2010, 20: 1. [16] Liu D, Sun D W, Zeng X A. Food and Bioprocess Technology, 2014, 7(2): 307. [17] ElMasry G, Wang N, Vigneault C. Postharvest Biology and Technology, 2009, 52(1): 1. [18] Huang M, Wan X, Zhang M, et al. Journal of Food Engineering, 2013, 116(1): 45. [19] Zhang X L, Liu F, He Y, et al. Sensors, 2012, 12(12): 17234. [20] Lee W H, Kim M S, Lee H, et al. Journal of Food Engineering, 2014, 130: 1. [21] ZHU Lu-ying, WU Wei-wei, SUN Jie, et al(朱路英, 吴伟伟, 孙 杰, 等). Food Science and Technology(食品科技), 2010, 31(21): 275.