基于高光谱成像技术的枇杷碰伤等级检测研究

doi:10.3964/j.issn.1000-0593(2023)06-1792-08

Abstract
References
Related Citation (15)

Download: PDF (4394 KB)
Export: BibTeX | EndNote (RIS)

Abstract Loquat is a freshwater fruit at the turn of spring and summer, it has a sour taste and can be eaten directly or made into candied fruit or wine, and it has the effect of resolving phlegm, relieving cough, harmonizing the stomach and lowering gas. The texture of loquat is soft and juicy, so it is prone to be bruised during picking, storage and transportation, resulting in economic losses. Therefore, detecting bruised loquats with high precision and rapid classification is essential. Meanwhile, we have used different methods to treat loquats with different bruising levels to reduce economic losses. The ones with light bruises can make loquat juice and paste. The ones with moderate bruises can be removed from damage region to make canned loquats for preservation. The ones with heavy bruises can be disposed of directly to save storage costs. At present, the bruise level of loquats is mainly discriminated by the operator’s naked eye. It is affected by personal habits, light intensity and subjective psychological factors, which will cause misclassification. In this paper, we propose a method based on hyperspectral imaging technology spectral combined with color features to classify loquat bruise level with high precision, rapidity and non-destructiveness. Firstly, we used a free-fall collision device to prepare light, moderate and heavy bruised loquat samples and used a hyperspectral imaging system to collect data. Secondly, we select the average spectrum of 100 pixels in the region of interest as the sample spectrum and preprocess the spectrum with MSC, which is used as the spectral feature for the subsequent model. Finally, we combined spectral features with color features and used RF, PLS-DA, ELM, and LS-SVM to build loquat bruising level models based on spectral features, RGB color features combined with spectral features, HSI color features combined with spectral features, and mixed color features combined with spectral features, respectively. Among all the above models, the loquat bruise level model based on mixed color features combined with spectral features has the best prediction effect. The overall recognition accuracy of the models using RF, PLS-DA, ELM and LS-SVM algorithms is 91.11%, 86.67%, 95.56%, and 100%, respectively. The RBF-LS-SVM bruising loquat model has the highest accuracy. The results show that the model based on single spectral features has the lowest accuracy, the model combined with RGB or HSI color features has higher accuracy, and the model based on spectral features combined with mixed color features has the highest accuracy. This study provides a certain theoretical reference and experimental basis for fruit bruising level discrimination.

Key words：Loquat; Hyperspectral imaging; Spectral features; Color features; Bruising level; Least squares support vector machine

Received: 2022-03-13 Accepted: 2022-06-10

ZTFLH:

O657.3

Corresponding Authors: LIU Yan-de E-mail: jxliuyd@163.com

Cite this article:

LI Bin,HAN Zhao-yang,WANG Qiu, et al. Research on Bruise Level Detection of Loquat Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1792-1799.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)06-1792-08 OR https://www.gpxygpfx.com/EN/Y2023/V43/I06/1792

[1] Besada C, Sanchez G, Gil R, et al. Food Research International, 2017, 100(Part 1): 234.
[2] LIU Li-li, LIU Yu-yin, WANG Jie, et a1(刘丽丽, 刘玉垠, 王杰, 等). Food Science(食品科学), 2020, 41(5): 306.
[3] Carlos B, Ignacio B, Esteban S, et al. Agricultural Water Management, 2018, 205: 1.
[4] WU Qiong, ZHOU Ran(吴琼，周然). Science and Technology of Food Industry(食品工业科技), 2017, 38(11): 356.
[5] Wang Nannan, Sun Dawen, Yang Yichao, et al. Food Analytical Methods, 2016, 9(1): 178.
[6] Zhao Yingqi, Men Sen, Liu Jiaxin, et al. Advance Journal of Food Science and Technology, 2016, 12(7): 388.
[7] Indurani C, Shubham S P, Lankapalli R, et al. Food Analytical Methods, 2019, 12(11): 2438.
[8] LI Guo-jin, DONG Di-yong, CHEN Shuang（李国进, 董第永, 陈双）. Journal of Agricultural Mechanization Research（农机化研究）, 2015，(10): 13.
[9] Li Jiangbo, Chen Liping. Computers and Electronics in Agriculture, 2017, 142: 524.
[10] Munera S, Gomez-Sanchis J, Aleixos N, et al. Postharvest Biology and Technology, 2021, 171: 111356.
[11] Lu Yuzhen, Lu Renfu, Zhang Zhao. Postharvest Biology and Technology, 2021, 180: 111624.
[12] LI Xiao-yu, XU Sen-miao, FENG Yao-yi, et al(李小昱, 徐森淼, 冯耀泽, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(1): 221.
[13] OUYANG Ai-guo, LIU Hao-chen, CHENG Long, et al(欧阳爱国, 刘昊辰, 成龙, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(8): 2598.
[14] WANG Zhi-yu(王志宇). Journal of Agricultural Mechanization Research（农机化研究）, 2022, 44(3): 38.
[15] Yusuf F, Olayiwola T, Afagwu C. Fluid Phase Equilibria, 2021, 531: 112898.
[16] Lou Shan, Jiang Xiangqian, Zeng Wenwen, et al. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2015，16: 395.
[17] Liu Wangyu, Xie Weigui, Dang Yanping, et al. Journal of Natural Fibers, 2020, 17(11): 1605.
[18] Saleem M H, Potgieter J, Arif K M. Precision Agriculture, 2021, 22(6): 2053.