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| Identification of Ginkgo Fruit Species by Hyperspectral Image Combined With PSO-SVM |
| ZHANG Fu1, 2, ZHANG Fang-yuan1, CUI Xia-hua1, WANG Xin-yue1, CAO Wei-hua1, ZHANG Ya-kun1, FU San-ling3* |
1. College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
2. Collaborative Innovation Center of Advanced Manufacturing of Machinery and Equipment of Henan Province, Luoyang 471003, China
3. College of Physical Engineering, Henan University of Science and Technology, Luoyang 471023, China
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Abstract Ginkgo fruit with antioxidant, anti-tumour and cardiovascular disease prevention functions is rich in vitamins, ginkgo lactones and ginkgo flavonoids, and can be used for both medicine and food. Due to the different varieties of Ginkgo fruit, the content of the main ingredientsis different and there are differences in quality. In addition, the content of certain components in ginkgo fruit has a greater impact on their storage and processing. In order to achieve efficient and non-destructive identification of ginkgo fruit varieties, the Support Vector Machine (SVM) classification model based on hyperspectral imaging technology was proposed, and Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) was used to optimizethe parameters of the model to improve the accuracy of species identification. In this study, 630 ginkgo fruits of three species were regarded as the research objects and divided into training and test sets according to 2∶1, with 420 and 210 samples respectively. The hyperspectral acquisition system acquired Ginkgo fruit images in the range of 900～1 700 nm. Then region of interest (ROI) of 25×25 pixel in the center of mass position was selected after black and white correction, and the average spectrum in the region was extracted as the original spectral data. Because of the large noise at both ends of the original spectra, the signal noise ratio was lower and the effective information was less. The spectral band in the range of 945.98~1 698.75 nm was intercepted as the effective band, which was pre-processed by Standard Normal Variate transformation (SNV). Successive Projection Algorithms (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to extract the characteristic wavelengths. The wavelength reflectivity was used as the input matrix X, and the sample varieties 1, 2, 3 were used as the output matrix Y. Six identification models were established for the SNV-SPA/CARS-(GA/PSO)-SVM. The experimental results showed that the SNV-CARS-PSO-SVM model had the best identification performance, and the classification accuracy was 96.67%, indicating that the characteristic wavelength variables extracted by CARS could represent all wavelength information, and the PSO-SVM model had a better species identification effect, which could realize the identification of ginkgo fruit. This study provides a new idea for the efficient and non-destructive identification of ginkgo fruit species.
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Received: 2022-09-03
Accepted: 2022-11-12
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Corresponding Authors:
FU San-ling
E-mail: fusanling@126.com
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[1] WU Ya-ping, CAO Meng, WANG Li(吴亚萍, 操 萌, 王 莉). Forest Science and Technology(林业科技通讯), 2021, (11): 46.
[2] CHEN Peng, HE Feng-ren, YU Bi-yu(陈 鹏, 何凤仁, 余碧钰). Journal of Yangzhou University(Agricultural and Life Science Edition)[扬州大学学报(农业与生命科学版)], 1999, (1): 32.
[3] ZHANG Feng-lun, ZHENG Qun-xiong, WU Su-ling, et al(张锋伦, 郑群雄, 吴素玲, 等). Jiangsu Agricultural Sciences(江苏农业科学), 2008, (4): 230.
[4] BAI Wei-dong, HUANG Guo-yong, KONG De-jun(白卫东, 黄国勇, 孔德俊). Science and Technology of Food Industry(食品工业科技), 2004, (8): 140.
[5] JIAO Jun, SHENG Yang, WANG Biao, et al(焦 俊, 圣 阳, 王 标, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2021, 58(22): 44.
[6] REN Chuan-yi, CHENG Jun-yong, CHEN Zhen-chao, et al(任传义, 程军勇, 陈振超, 等). Forest Research(林业科学研究), 2017, 30(5): 779.
[7] Zhang Xiaoai, Hu Yue, Liu Liping, et al. IOP Conference Series: Earth and Environmental Science, 2019, 332(3): 032026.
[8] Yang Haoyan, Ni Jiangong, Gao Jiyue, et al. Scientific Reports, 2021, 11(1): 15756.
[9] BAI Wen-ming, WANG Lai-bing, CHENG Ri-qing, et al(白文明, 王来兵, 成日青, 等). Chinese Journal of Pharmaceutical Analysis(药物分析杂志), 2018, 38(10): 1661.
[10] SONG Jun, WANG Dong, GUO Ling-an, et al(宋 君, 王 东, 郭灵安, 等). Chinese Journal of Analysis Laboratory(分析试验室) , 2014, 33(3): 292.
[11] YANG Shu-qin, PENG Kang, LIU Xu(杨蜀秦, 彭 康, 刘 旭). Journal of South China Agricultural University(华南农业大学学报), 2018, 39(5): 110.
[12] ZHANG Han-wen, LI Ye, JIANG Sheng, et al(张瀚文, 李 野, 江 晟, 等). Journal of Jilin University(Science Edition)[吉林大学学报(理学版)], 2022, 60(3): 655.
[13] ZHENG Jie, RU Chen-lei, ZHANG Lu, et al(郑 洁, 茹晨雷, 张 璐, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2021, 46(10): 2571.
[14] ZHANG Chu, LIU Fei, KONG Wen-wen, et al(张 初, 刘 飞, 孔汶汶, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(20): 270.
[15] ZHANG Lu, RU Chen-lei, YIN Wen-jun, et al(张 璐, 茹晨雷, 殷文俊, 等). Chinese Journal of Pharmaceutical Analysis(药物分析杂志), 2021, 41(4): 726.
[16] ZHANG Lin-zhong, DING Ling-ling, CAI Xue-zhen, et al(章林忠, 丁玲玲, 蔡雪珍, 等). Journal of Anhui Agricultural University(安徽农业大学学报), 2019, 46(1): 160.
[17] BAO Yi-dan, CHEN Na, HE Yong, et al(鲍一丹, 陈 纳, 何 勇, 等). Optics and Precision Engineering(光学精密工程), 2015, 23(2): 349.
[18] ZHOU Cong, WANG Hui, YANG Jian, et al(周 聪, 王 慧, 杨 健, 等). China Journal of Chinese Materia Medica(中国中药杂志)，2022, 47(22): 6027.
[19] Zou S, Tseng Y C, Zare A, et al. Biosystems Engineering, 2019, 188: 165.
[20] Ríos-Reina R, Callejón R M, Amigo J M. Food Control, 2021，130：108365.
[21] TAN Ke-zhu, CHAI Yu-hua, SONG Wei-xian, et al(谭克竹, 柴玉华, 宋伟先, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(9): 235.
[22] Moscetti R, Saeys W, Keresztes J C, et al. Food and Bioprocess Technology, 2015, 8(7): 1593.
[23] ZHAO Fan, YAN Zhao-ru, SONG Hai-yan(赵 凡, 闫昭如, 宋海燕). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(7): 2240.
[24] WANG Jing-hui, CHENG Jiao-jiao, LIU Yang, et al(王靖会, 程娇娇, 刘 洋, 等). Journal of Agricultural Science and Technology(中国农业科技导报), 2021, 23(9): 121.
[25] SHEN Yu, FANG Sheng, WANG Feng-yun, et al(沈 宇, 房 胜, 王风云, 等). Journal of Agricultural Science and Technology(中国农业科技导报), 2020, 22(3): 64.
[26] LI Rui, FU Long-sheng(李 瑞, 傅隆生). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(S1): 362.
[27] PAN Xi, LI Ran, WEI Min, et al(潘 曦, 李 冉, 魏 敏, 等). Food & Machinery(食品与机械), 2022, 38(7): 85.
[28] MO Song-tao, DONG Tao, ZHAO Xi-xuan, et al(莫松涛, 董 涛, 赵汐璇, 等). Journal of Fruit Science(果树学报), 2022, 39(12): 2418.
[29] CHENG Tian-tian, WANG Ke-jian, HAN Xian-zhong, et al(成甜甜, 王克俭, 韩宪忠, 等). Food & Machinery(食品与机械), 2020, 36(11): 46.
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