Abstract
The quality of tomato products is significantly degraded due to defects on raw processing tomatoes such as insect hole or mildew. This research aims to investigate the potential of using visible/ near infrared (Vis/NIR) hyperspectral imaging for detection of insect hole and mildew on raw processing tomato. Tomato samples were imaged using a hyperspectral imaging system that covers a spectral range from 408 to 1013 nm. To images, region of interests (ROIs) were manually selected to extract mean spectra on every individual samples. Principal component analysis (PCA) was performed on the extracted spectra to select three optimal wavelengths (550, 750, 900 nm) for defects detection. PCA and pair-wise band ratio analysis were conducted on the spectral images using the optimal wavelengths to generate PC and band-ratio images, respectively. Masking, threshold-based segmentation, and morphologic operations were applied on the generated images to identify defective areas on the tomato surface. The accuracies of identifying insect hole, mildew, and healthy tomato achieved 93.3%, 90%, and 100% in the PC images, and 93.3%, 96.7%, and 100% in the band-ratio images, respectively. Therefore, the Vis-NIR hyperspectral imaging could be an effective approach for detecting insect hole and mildew on the surface of raw tomatoes. In addition, online detection system could be benefit by using the wavelengths of 550 nm and 750 nm.
Publication Date
6-28-2017
First Page
135
Last Page
138,179
DOI
10.13652/j.issn.1003-5788.2017.06.027
Recommended Citation
Yan, MA; Ruoyu, ZHANG; and Yanjie, QI
(2017)
"Detection of insect hole andmildew in processing tomato by visible near infrared hyperspectral imaging,"
Food and Machinery: Vol. 33:
Iss.
6, Article 27.
DOI: 10.13652/j.issn.1003-5788.2017.06.027
Available at:
https://www.ifoodmm.cn/journal/vol33/iss6/27
References
［1］ 李艳, 王建江, 曾沂辉, 等. 多用途加工番茄新品种石红14号的选育［J］. 新疆农业科学, 2008, 45(S1): 121-123.
［2］ ZENG Zhong-da, LIANG Yi-zeng, WANG Ya-li, et al. Alternative moving window factor analysis for comparison analysis between complex chromatographic data［J］. Journal of Chromatography A, 2006, 1 107(1): 273-285.
［3］ ALTISENT M R, GARCIA L R, MOREDA G P, et al. Sensors for product characterization and quality of specialty crops: A review［J］. Computers and Electronics in Agriculture, 2010, 74(2): 176-194.
［4］ 李江波, 饶秀勤, 应义斌, 等. 农产品外部品质无损检测中高光谱成像技术的应用研究进展［J］. 光谱学与光谱分析, 2011, 31(8): 2 021-2 026.
［5］ 李江波, 饶秀勤, 应义斌, 等. 基于高光谱成像技术检测脐橙溃疡［J］. 农业工程学报, 2010, 26(8): 222-228.
［6］ 刘德华, 张淑娟, 王斌, 等. 基于高光谱成像技术的山楂损伤和虫害缺陷识别研究［J］. 光谱学与光谱分析, 2015, 35(11): 3 167-3 171.
［7］ 王婉娇, 贺晓光, 王松磊, 等. 基于高光谱成像技术的灵武长枣常见缺陷检测［J］. 食品与机械, 2015, 31(3): 62-65, 86.
［8］ 单佳佳, 彭彦昆, 王伟, 等. 基于高光谱成像技术的苹果内外品质同时检测［J］. 农业机械学报, 2011, 42(3): 140-144.
［9］ XING J, NGADI M, WANG N, et al. Wavelength selection for surface defects detection on tomatoes by means of a hyperspectral imaging system［C］//2006 ASAE Annual Meeting.［S. l.］: American Society of Agricultural and Biological Engineers, 2006: 1.
［10］ DANHEE J, KIM M S, HOONSOO L, et a1. Detection Algorithm for Cracks on the Surface of Tomatoes using Multispectral Vis/NIR Reflectance Imagery［J］. Journal of Biosystems Engineering, 2013, 38(3): 199-207.
［11］ HOONSOO L, KIM M S, DANHEE J, et al. Detection of Cra-cks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System［J］. Sensors (Basel), 2014, 14(10): 18 837-18 850.
［12］ 郭红艳, 刘贵珊, 吴龙国, 等. 基于高光谱成像的马铃薯环腐病无损检测［J］. 食品科学, 2016, 37(12): 203-207.
［13］ 张若宇, 饶秀勤, 高迎旺, 等. 基于高光谱漫透射成像整体检测番茄可溶性固形物含量［J］. 农业工程学报, 2013, 29(23): 247-252.
［14］ 李波, 刘占宇, 黄敬峰, 等. 基于PCA和PNN的水稻病虫害高光谱识别［J］. 农业工程学报, 2009, 25(9): 143-147.
［15］ 公丽艳, 孟宪军, 刘乃侨, 等. 基于主成分与聚类分析的苹果加工品质评价［J］. 农业工程学报, 2014, 30(13): 276-285.
［16］ 苏文浩, 刘贵珊, 何建国, 等. 高光谱图像技术结合图像处理方法检测马铃薯外部缺陷［J］. 浙江大学学报: 农业与生命科学版, 2014, 40(2): 188-196.
［17］ 赵进辉, 吁芳, 吴瑞梅, 等. 基于分段主成分分析与波段比的鸡胴体表面粪便污染物检测［J］. 激光与光电子学进展, 2011, 48(7): 166-170.
［18］ 蔡健荣, 王建黑, 黄星奕, 等. 高光谱图像技术检测柑橘果锈［J］. 光电工程, 2009, 36(6): 26-30.
［19］ 蔡健荣, 王建黑, 陈全胜, 等. 波段比算法结合高光谱图像技术检测柑橘果锈［J］. 农业工程学报, 2009, 25(1): 127-131.