基于高光谱图谱融合技术的黄桃损伤程度判别研究

doi:10.3964/j.issn.1000-0593(2023)02-0435-07

Abstract
References
Related Citation (15)

Download: PDF (9297 KB)
Export: BibTeX | EndNote (RIS)

Abstract Yellow peaches are rich in nutrients and very popular with consumers. However, they are susceptible to bruising during processing and transportation, causing great economic losses to farmers and sellers. When using hyperspectral technology to detect fruit bruises, researchers only use spectral information or image features to build a bruise detection model but rarely use spectral fusion technology for fruit bruise detection. Moreover, spectral reflectance is easily affected by external stray light, some real information will be lost, and the image contains little information, It is difficult to identify images only relying on a few specific bands accurately. Therefore, to achieve an accurate classification of the degree of bruising of yellow peaches, different treatment methods can be developed according to the different degrees of bruising, and economic damage can be reduced. This study proposes a model that uses the spectral information of hyperspectral combined with image features to detect yellow peaches with different degrees of bruising. In this experiment, 180 yellow peaches were used as experimental samples. Firstly, the images of lightly, moderately and severely bruised yellow peaches were collected using hyperspectral images, A 100×100 pixel area was selected as the region of interest at the bruise location of each yellow peach, and the spectral information of the bruised area was extracted using ENVI4.5 software. Then, the principal component analysis (PCA) algorithm was used to reduce the dimensionality of the collected hyperspectral images, and the PC1 image was finally selected as the principal component image of the bruised yellow peaches among the first 5 PC images. The images corresponding to 6 characteristic wavelengths were selected as the feature images according to the weight coefficient curve of PC1 image, and the average grey value was used as the image feature of the bruised yellow peaches. Finally, PLS-DA models for yellow peach bruising were established using spectral information, image features and spectral information combined with image features respectively, and the performance of each model was evaluated using the correct classification rate. The results showed that the PLS-DA model based on spectral information combined with image features had the best discriminative effect, with classification accuracy of 85%, 90% and 100% for light bruises, moderate bruises and severe bruises respectively, and the acorrectoverall rate reached 91.7%. In order to further improve the accuracy and operational efficiency of the PLS-DA model, this paper uses the competitive adaptive reweighted sampling (CARS) algorithm to filter the spectral data in the fused data for the feature bands. The model built using feature bands combined with image features had the best classification prediction, with 95%, 90% and 95% correct predictions for lightly bruised, moderately bruised and heavily bruised yellow peaches, respectively, and acorrectoverall rate of 93.3%. In conclusion, this study shows that it is feasible to establish a PLS-DA model based on hyperspectral spectral data combined with image features to detect the degree of bruising of yellow peaches, which provides a theoretical basis for the post-harvest treatment of yellow peaches.

Key words：Yellow peaches bruise; Hyperspectral imaging; Spectral information; Image features; Damage degree; Partial least squares linear discriminant

Received: 2021-11-17 Accepted: 2022-04-20

ZTFLH:

O657.3

Corresponding Authors: OUYANG Ai-guo E-mail: ouyang1968711@163.com

Cite this article:

LI Bin,ZHANG Feng,YIN Hai, et al. Study on Damage Degree Discrimination of Yellow Peach Based on Hyperspectral Map Fusion Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 435-441.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)02-0435-07 OR https://www.gpxygpfx.com/EN/Y2023/V43/I02/435

[1] Garcia-Ramos F J, Ortiz-Canavate J, Ruiz-Altisent M. Applied Engineering in Agriculture, 2003, 19(6): 703.
[2] Tan W, Sun L, Yang F, et al. Journal of Chemometrics, 2018, 32(10): e3067.
[3] ElMasry G M, Nakauchi S. Biosystems Engineering, 2016, 142: 53.
[4] Mahesh S, Jayas D S, Paliwal J, et al. Journal of Stored Products Research, 2015, 61: 17.
[5] Wang N N, Sun D W, Yang Y C, et al. Food Analytical Methods, 2016, 9(1): 178.
[6] Tan W, Sun L, Yang F, et al. Optik, 2018, 154: 581.
[7] WU Long-guo, WANG Song-lei, KANG Ning-bo, et al(吴龙国, 王松磊, 康宁波，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(20): 281.
[8] Zhu X, Li G. International Journal of Food Properties, 2019, 22(1): 1709.
[9] Li J, Chen L, Huang W. Postharvest Biology and Technology, 2018, 135: 104.
[10] HUANG Feng-hua, ZHANG Shu-juan, YANG Yi, et al(黄锋华, 张淑娟, 杨一，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015, 46(11): 252.
[11] LIU Yan-de, HAN Ru-bing, ZHU Dan-ning, et al(刘燕德, 韩如冰, 朱丹宁，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(10): 3175.
[12] SUN Jun, JIN Xia-ming, MAO Han-ping, et al(孙俊, 金夏明, 毛罕平，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(10): 167.
[13] GUO Zhi-ming, HUANG Wen-qian, PENG Yan-kun, et al(郭志明, 黄文倩, 彭彦昆，等). Modern Food Science and Technology(现代食品科技), 2014, 30(8): 59.
[14] Yin S, Bi X, Niu Y, et al. Emirates Journal of Food and Agriculture, 2017, 29(8): 601.
[15] SUN Jie, DING Xiao-jun, DU Lei, et al(孙洁, 丁笑君, 杜磊，等). Journal of Textile Research(纺织学报), 2019, 40(12): 146.
[16] CHI Qian, WANG Zhuan-wei, YANG Ting-ting, et al(迟茜, 王转卫, 杨婷婷，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015, 46(3): 235.
[17] YANG Ting-ting, CHI Qian, WANG Zhuan-wei, et al(杨婷婷, 迟茜, 王转卫，等). Journal of Agricultural Mechanization Research(农机化研究), 2015, 37(5): 44.