|
|
|
|
|
|
Rice Germination Rate Detection Based on Fluorescent Spectrometry and Deep Belief Network |
LU Wei, GUO Yang-ming, DAI De-jian, ZHANG Cheng-yu, WANG Xin-yu |
College of Engineering, Jiangsu Province Engineering Laboratory of Modern Facility Agriculture Technology and Equipment, Nanjing Agricultural University, Nanjing 210031, China |
|
|
Abstract Traditional rice seed germination rate detection methods have low efficiency, poor accuracy and high specialization. The paper proposed a novel method by using fluorescent spectrometry combined with Deep Belief Network (DBN) to establish forecasting model for rice seed germination rate. Firstly, two varieties of seeds, Lianjing 7 and Wuyunjing, with 0~7 artificial aged days separately were soaked into purified water for 5~30 minutes with every 5 minutes’ interval. Then the fluorescence spectrums of the soak solutions were detected using fluorescence spectrometer. In addition, the spectrum data were centralized and then denoised with Ensemble Empirical Mode Decomposition (EEMD). The characteristic fluorescence wavelength of 441.5nm was extracted using Principal Component Anamysis (PCA). Finally, the rice seed germination predicting models were establishee with Partial Least Squares Regression (PLSR), Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN) and Deep Belief Network (DBN), respectively. The results showed that the accuracy of DBN model was the highest in the case of less data and weak signal, and Rp=0.979 2, RMSEP=0.101. At the same time,we got the best soaking time is 22.1 min by analyzing the changing trend of mixed rice seed fluorescent data Rp, actually, it took about 5 min to get the accuracy more than 0.95 (Rp). The research results demonstrated the feasibility and high accuracy for predicting rice seed germination rate non-invasively by combining the fluorescent spectrometry and EEMD-DBN model, moreover, it adapts to the detection of rice seeds with different colors and contaminated levels.
|
Received: 2017-06-21
Accepted: 2017-11-19
|
|
|
[1] Sharif M K, Butt M S, Anjum F M, et al. Critical Reviews in Food Science and Nutrition, 2014, 54(6): 807.
[2] Paiva E P, Torres S B, Almeida J P N D, et al. Revista Ciência Agronmica, 2017, 48(1):118.
[3] Na Y W, Shim S I, Chung J S, et al. Korean Journal of Crop Science, 2014, 59(2):188.
[4] Lohumi S, Mo C, Cho B K, et al. Journal of Biosystems Engineering, 2013, 38(4): 312.
[5] Song L, Wang Q, Wang C, et al. Journal of Stored Products Research, 2015, 62: 46.
[6] Yu S M, Lu W, Ding D, et al. Laser & Optoelectronics Progress, 2016, 53(1): 113.
[7] Fang W H, Lu W, Xu H L, et al. Spectroscopy and Spectral Analysis, 2016, 36(8): 2692.
[8] Li H, Lu W, Du C, et al. Chinese Journal of Lasers, 2015, 42(11): 1115003.
[9] Fang W, Lu W, Hong D, et al. Optics Journal, 2015, 35(10): 111.
[10] Lu L, Tian S, Zhang J, et al. PLoS One, 2013, 8(2): e57360.
[11] Ambrose A, Lohumi S, Lee W H, et al. Sensors & Actuators B Chemical, 2016, 224: 500.
[12] Yang J, Gong W, Shi S, et al. Scientific Reports, 2016, 6: 28787.
[13] Chen Q, Qi S, Li H, et al. Spectrochimica Acta Part A Molecular & Biomolecular Spectroscopy, 2014, 131(19):177.
[14] Agati G, Foschi L, Grossi N, et al. European Journal of Agronomy, 2013, 45: 39.
[15] Sergiel I, Pohi P, Biesaga M, et al. Food Chemistry, 2014, 145: 319.
[16] Ko K Y, Lee C A, Choi J C, et al. Food Additives & Contaminants Part A Chemistry Analysis Control Exposure & Risk Assessment, 2014, 31(9): 1451.
[17] Singhal V, Gogna A, Majumdar A. Deep Dictionary Learning vs Deep Belief Network vs Stacked Autoencoder: An Empirical Analysis. International Conference on Neural Information Processing. Springer International Publishing, 2016. 337.
[18] Shaban M. International Journal of Agriculture and Crop Sciences, 2013, 6(11): 627.
[19] Kaewkham T, Russell K H, Boonmee S. Biocontrol Science and Technology, 2016, 26(8): 1048.
[20] Yang H, Fu Y B. Frontiers in Plant Science, 2016, 7: 1474.
[21] Mala D M, Yoshimura M, Kawasaki S, et al. LWT-Food Science and Technology, 2016, 68: 14.
[22] Zhang Y, Liu B, Ji X, et al. Neural Processing Letters, 2017, 45(2): 365.
[23] vokelj M, Samo Z, Ivan P. Journal of Sound and Vibration, 2016, 370: 394.
[24] Jolliffe I T, Jorge C. Phil. Trans. R. Soc. A, 2016, 374: 20150202.
[25] Bro R, Age K S. Analytical Methods, 2014, 6(9): 2812.
[26] Wang L, Zeng Y, Chen T. Expert Systems with Applications, 2015, 42(2): 855.
[27] Lian R J. IEEE Transactions on Industrial Electronics, 2014, 61(3): 1493.
[28] O’Connor P, Neil D, Liu S C, et al. Frontiers in Neuroscience, 2013, 7(178): 1.
[29] Kuremoto Takashi, Shinsuke K, Kunikazu K, et al. Neurocomputing, 2014, 137: 47.
[30] Elena A G, Alexander N T, Folkert A H. Plant Physiology, 1997, 114: 383.
[31] Olivier L, Frans J M H, Julia B, et al. Plant Physiology, 2000, 122(2): 597.
[32] Brits G J, Brown N A C, Calitz F J, et al. South African Journal of Botany, 2015, 97: 1.
[33] Zhao Q, Lv Q, Wang H. Biosensors and Bioelectronics, 2015, 70: 188. |
[1] |
LEI Hong-jun1, YANG Guang1, PAN Hong-wei1*, WANG Yi-fei1, YI Jun2, WANG Ke-ke2, WANG Guo-hao2, TONG Wen-bin1, SHI Li-li1. Influence of Hydrochemical Ions on Three-Dimensional Fluorescence
Spectrum of Dissolved Organic Matter in the Water Environment
and the Proposed Classification Pretreatment Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 134-140. |
[2] |
XIA Ming-ming1, 2, LIU Jia3, WU Meng1, 2, FAN Jian-bo1, 2, LIU Xiao-li1, 2, CHEN Ling1, 2, MA Xin-ling1, 2, LI Zhong-pei1, 2, LIU Ming1, 2*. Three Dimensional Fluorescence Characteristics of Soluble Organic Matter From Different Straw Decomposition Products Treated With Calcium Containing Additives[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 118-124. |
[3] |
GU Yi-lu1, 2,PEI Jing-cheng1, 2*,ZHANG Yu-hui1, 2,YIN Xi-yan1, 2,YU Min-da1, 2, LAI Xiao-jing1, 2. Gemological and Spectral Characterization of Yellowish Green Apatite From Mexico[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 181-187. |
[4] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
[5] |
LIU Wei1, 2, ZHANG Peng-yu1, 2, WU Na1, 2. The Spectroscopic Analysis of Corrosion Products on Gold-Painted Copper-Based Bodhisattva (Guanyin) in Half Lotus Position From National Museum of China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3832-3839. |
[6] |
WANG Hong-jian1, YU Hai-ye1, GAO Shan-yun1, LI Jin-quan1, LIU Guo-hong1, YU Yue1, LI Xiao-kai1, ZHANG Lei1, ZHANG Xin1, LU Ri-feng2, SUI Yuan-yuan1*. A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3710-3718. |
[7] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[8] |
SONG Yi-ming1, 2, SHEN Jian1, 2, LIU Chuan-yang1, 2, XIONG Qiu-ran1, 2, CHENG Cheng1, 2, CHAI Yi-di2, WANG Shi-feng2,WU Jing1, 2*. Fluorescence Quantum Yield and Fluorescence Lifetime of Indole, 3-Methylindole and L-Tryptophan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3758-3762. |
[9] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
[10] |
YI Min-na1, 2, 3, CAO Hui-min1, 2, 3*, LI Shuang-na-si1, 2, 3, ZHANG Zhu-shan-ying1, 2, 3, ZHU Chun-nan1, 2, 3. A Novel Dual Emission Carbon Point Ratio Fluorescent Probe for Rapid Detection of Lead Ions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3788-3793. |
[11] |
YANG Ke-li1, 2, PENG Jiao-yu1, 2, DONG Ya-ping1, 2*, LIU Xin1, 2, LI Wu1, 3, LIU Hai-ning1, 3. Spectroscopic Characterization of Dissolved Organic Matter Isolated From Solar Pond[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3775-3780. |
[12] |
QI Guo-min1, TONG Shi-qian1, LIN Xu-cong1, 2*. Specific Identification of Microcystin-LR by Aptamer-Functionalized Magnetic Nanoprobe With Laser-Induced Fluorescence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3813-3819. |
[13] |
HE Yan-ping, WANG Xin, LI Hao-yang, LI Dong, CHEN Jin-quan, XU Jian-hua*. Room Temperature Synthesis of Polychromatic Tunable Luminescent Carbon Dots and Its Application in Sensitive Detection of Hemoglobin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3365-3371. |
[14] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
[15] |
LI Xiao-li1, WANG Yi-min2*, DENG Sai-wen2, WANG Yi-ya2, LI Song2, BAI Jin-feng1. Application of X-Ray Fluorescence Spectrometry in Geological and
Mineral Analysis for 60 Years[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2989-2998. |
|
|
|
|