| 光谱学与光谱分析 |
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| Study on the Prediction of Germination Rate of Rice Seeds with Continuous Polarization Spectroscopy |
| CHENG Yu-qiong1, LU Wei1, 2*, HONG De-lin3, DANG Xiao-jing3, LUO Hui1 |
1. College of Engineering, Jiangsu Province Engineering Laboratory of Modern Facility Agriculture Technology and Equipment, Nanjing Agricultural University, Nanjing 210031, China
2. Key Laboratory of Jiangsu Province for Remote Measurement and Control Technology, Nanjing 210096, China
3. College of Agriculture，State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China |
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Abstract With respect to the problem of long period and low precision in using traditional methods to predict rice seeds germination rate, a novel method based on continuous polarization spectroscopy was proposed to achieve rapid and nondestructive prediction .The paper set different aging rice seeds as prediction targets and ten minutes as prediction time, using polarizer to modulate optical fiber collimating light source to linearly polarized light which issuing into rice seeds extract vertically before rotating the analyser every 5 degrees . The transmission spectrum was predicted through the optical fiber spectrometer. After normalization pretreatment to the polarization spectrum, the article gave the characteristics of polarization angel and wavelength by 0 degree, 5 degrees, 25 degrees, 620, 788 and 576 nm according to the contribution of polarization angel and wavelength when predicting different germination rate rice seeds and inputted obtained continuous polarization spectrum by wavelength, polarization angel, transmissivity to construct rice seeds germination rate prediction model using three modeling methods to build rice seeds germination rate prediction model in comparison, including Partial Least Squares Regression (PLSR), Back Propagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN).1 520 sets of experimental data were measured in total at different polarization angels through using rice seeds with different aging days (0, 2, 4, 6) respectively, setting 912 sets of data as calibration set and 608 sets of data as predicion set. The modeling results show that RBF model’s prediction accuracy is the highest. Its correlation coefficient is 0.976; the mean square is 0.785; and the average relative error is 0.85%. The research results show that the continuous polarization spectroscopy technique through multidimension spectral information can achieve rapid and accurate prediction of rice seeds germination rate.
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Received: 2015-03-30
Accepted: 2015-07-22
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Corresponding Authors:
LU Wei
E-mail: njaurobot@njau.edu.cn
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