|
|
|
|
|
|
Physical and Chemical Indexes Were Determined Based on Multispectral Image Angle Fusion |
LIU Hong-yang1, 2, KONG De-guo1, 2*, LUO Hua-ping1, 2, GAO Feng1, 2, WANG Cong-ying1, 2 |
1. College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
2. Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region,Alar 843300, China
|
|
|
Abstract Based on a multispectral image angle fusion. Multispectral images were obtained from 10° to 90 °at 10° intervals. Multispectral image angle fusion and the region of interest (ROI) were extracted using ENVI5.1 software to obtain the multispectral data. The Pearson correlation analysis of the spectral reflectance, band, and relative azimuth found that both the band and relative azimuth were extremely significantly correlated with the spectral reflectance, and the relative azimuth correlation coefficient of 0.1 is greater than the band correlation coefficient of 0.053. Therefore, it is necessary to add the relative azimuth factors in the modeling process. Using standard normal variable transformation (SNV), mean centralization transformation (MC), convolution smoothing treatment (S_G), normalization treatment (Nor), partial least squares regression (PLSR) to evaluate the full band set correlation coefficient (Rc), prediction set correlation coefficient (Rp), correction set root mean square error (RMSEC) and prediction set root mean square error (RMSEP) to explore the effect of the model. The results show that the prediction effect of the established PLSR and SVM models is significantly improved after adopting the angle fusion treatment. The optimal model is a partial least squares regression model (AF-PLSR) with Rc of 0.936, RMSEC of 0.298, Rp of 0.901, RMSEP of 0.285; the optimal prediction model is the support vector machine model (AF-SVM), Rc is 0.894, 0.527, 0.376; Rp is 0.830, 0.901, and RMSEP is 0.532, 0.379 respectively. Angle fusion combines the spectral data from different angles together to obtain more abundant information than a single angle and a more perfect spectral information. The established detection model has a higher accuracy. The results proved that it is feasible to predict the water content, hardness, and sugar content of Korla's fragrant pear based on the multispectral image angle fusion technology. The results provide a new idea for improving MMS and HMS NDE accuracy.
|
Received: 2022-08-22
Accepted: 2022-10-31
|
|
Corresponding Authors:
KONG De-guo
E-mail: 461080623@qq.com
|
|
[1] JING Chun-zhi(井春芝). Xinjiang Forestry(新疆林业), 2018,(3): 25.
[2] GAO Sheng, WANG Qiao-hua, FU Dan-dan, et al(高 升, 王巧华, 付丹丹,等). Acta Optica Sinica(光学学报), 2019, 39(10): 1030004.
[3] WANG Dan, ZHAO Peng, SUN Jia-bo, et al(王 丹,赵 朋,孙家波,等). Shandong Agricultural Sciences(山东农业科学), 2021, 53(6): 121.
[4] SUN Hong, ZHAO Yi, ZHANG Meng, et al(孙 红,赵 毅,张 猛,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(S2): 186.
[5] DONG Jian-wei, LIU Yuan-yuan, CHEN Fei, et al(董建伟,刘媛媛,陈 斐,等). Journal of Agricultural Mechanization Research(农机化研究), 2021, 43(9): 35.
[6] YANG Sheng-hui, ZHENG Yong-jun, LIU Xing-xing, et al(杨圣慧, 郑永军, 刘星星,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(10): 3220.
[7] DAI Jia-wei, WANG Hai-peng, CHEN Pu, et al(戴嘉伟,王海朋,陈 瀑,等). Chinese Journal of Analytical Chemistry(分析化学), 2022, 50(6): 839.
[8] ZHANG Zuo-jing, FU Xin-yang, CHEN Ke-ming, et al(张佐经, 付新阳, 陈柯铭,等). Food and Fermentation Industries(食品与发酵工业), 2022, 48(15): 281.
[9] ZHANG Hui, ZHANG Wen-wei, ZHANG Yong-yi, et al(张 慧, 张文伟, 张永毅,等). Acta Tabacaria Sinica (中国烟草学报), 2022, 28(3): 72.
[10] ZHANG Fan, SHU Ying, ZHANG Zhi-sheng, et al(张 凡, 淑 英, 张志胜,等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2021, 21(11): 191.
[11] JIN Rui, LI Xiao-yu, YAN Yi-yun(金 瑞, 李小昱, 颜伊芸). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(16): 258.
[12] Li Q, Huang Y, Zhang J, et al. Spectrochim. Acta, Part A, 2021, 247: 119119.
[13] Ge W Z, Zhang L, Li X L, et al. Biosyst. Eng., 2021, 210: 299.
[14] Rios-Reina R, Callejon R M, Savorani F, et al. Talanta, 2019, 198: 560.
[15] YAN Guang-jian, JIANG Hai-lan, YAN Kai(阎广建, 姜海兰, 闫 凯). National Remote Sensing Bulletin(遥感学报), 2021, 25(1): 83.
|
[1] |
FU Xiao-fen1, SONG You-gui1, 2*, ZHANG Ming-yu3, FENG Zhong-qi4, ZHANG Da-cheng4, LIU Hui-fang1. Application of Laser-Induced Breakdown Spectroscopy in Quantitative
Analysis of Sediment Elements[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 641-648. |
[2] |
ZHANG Mei-ling, CHEN Yong-jie, WANG Min-juan, LI Min-zan, ZHENG Li-hua*. A Hyperspectral Deep Learning Model for Predicting Anthocyanin
Content in Purple Leaf Lettuce[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 865-871. |
[3] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[4] |
WANG Wen-song1, PEI Chen-xi2, YANG Bin1*, WANG Zhi-xin2, QIANG Ke-jie2, WANG Ying1. Flame Temperature and Emissivity Distribution Measurement MethodBased on Multispectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3644-3652. |
[5] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[6] |
WU Yong-qing1, 2, TANG Na1, HUANG Lu-yao1, CUI Yu-tong1, ZHANG Bo1, GUO Bo-li1, ZHANG Ying-quan1*. Model Construction for Detecting Water Absorption in Wheat Flour Using Vis-NIR Spectroscopy and Combined With Multivariate Statistical #br#
Analyses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2825-2831. |
[7] |
LIU Rui-min, YIN Yong*, YU Hui-chun, YUAN Yun-xia. Extraction of 3D Fluorescence Feature Information Based on Multivariate Statistical Analysis Coupled With Wavelet Packet Energy for Monitoring Quality Change of Cucumber During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2967-2973. |
[8] |
ZHANG Hai-liang1, XIE Chao-yong1, TIAN Peng1, ZHAN Bai-shao1, CHEN Zai-liang1, LUO Wei1*, LIU Xue-mei2*. Measurement of Soil Organic Matter and Total Nitrogen Based on Visible/Near Infrared Spectroscopy and Data-Driven Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2226-2231. |
[9] |
LIU Mei-jun, TIAN Ning*, YU Ji*. Spectral Study on Mouse Oocyte Quality[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1376-1380. |
[10] |
DENG Xiao-jun1, 2, MA Jin-ge1, YANG Qiao-ling3, SHI Yi-yin1, HUO Yi-hui1, GU Shu-qing1, GUO De-hua1, DING Tao4, YU Yong-ai5, ZHANG Feng6. Visualized Fast Identification Method of Imported Olive Oil Quality Grade Based on Raman-UV-Visible Fusion Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1117-1125. |
[11] |
FENG Hai-kuan1, 2, TAO Hui-lin1, ZHAO Yu1, YANG Fu-qin3, FAN Yi-guang1, YANG Gui-jun1*. Estimation of Chlorophyll Content in Winter Wheat Based on UAV Hyperspectral[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3575-3580. |
[12] |
ZHANG Yuan-zhe1, LIU Yu-hao1, LU Yu-jie1, MA Chao-qun1, 2*, CHEN Guo-qing1, 2, WU Hui1, 2. Study on the Spectral Prediction of Phosphor-Coated White LED Based on Partial Least Squares Regression[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2347-2352. |
[13] |
ZHONG Xiang-jun1, 2, YANG Li1, 2*, ZHANG Dong-xing1, 2, CUI Tao1, 2, HE Xian-tao1, 2, DU Zhao-hui1, 2. Effect of Different Particle Sizes on the Prediction of Soil Organic Matter Content by Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2542-2550. |
[14] |
JIANG Qing-hu1, LIU Feng1, YU Dong-yue2, 3, LUO Hui2, 3, LIANG Qiong3*, ZHANG Yan-jun3*. Rapid Measurement of the Pharmacological Active Constituents in Herba Epimedii Using Hyperspectral Analysis Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1445-1450. |
[15] |
FAN Nai-yun, LIU Gui-shan*, ZHANG Jing-jing, YUAN Rui-rui, SUN You-rui, LI Yue. Rapid Determination of TBARS Contents in Tan Mutton Using Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 713-718. |
|
|
|
|