[Study on the Prediction of Germination Rate of Rice Seeds with Continuous Polarization Spectroscopy].

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.

Study on the Detection of Rice Seed Germination Rate Based on Infrared Thermal Imaging Technology Combined with Generalized Regression Neural Network.

On the basis of the differences in physiology and physics of rice seed with different aging time, the paper proposes a fast and nondestructive method which is based on infrared thermal imaging technology and generalized regression neural network to detect the germination rate of rice seed. This method solves the problems of long experimental period, complex operations and other disadvantages of the traditional method which is used to detect germination rate. When the temperature is 45 ℃ and humidity is 90%, the rice seeds are aged for 0, 1, 2, 3, 4, 5, 6 and 7 d respectively to get rice seeds of different germination rate. The data of 144 groups was extracted from the germ of rice seed. This data was divided into two groups randomly: the calibration set was 96 groups and the prediction set was 48 groups. Through analyzing and comparing the differences of infrared thermal image of rice seeds of different aging days, the relationship in physics and physiology between germination rate of rice seed and infrared thermal images was revealed. The infrared prediction model for germination rate of rice seed was established by combining partial least squares algorithm, Back Propagationneural network and General regression neural network. The result shows that the optimal germination rate model is built with GRNN. In this model, the correlation coefficient (RC) and standard deviation (SEC) of calibration sets are 0.932 0 and 2.056 0. At the same time, the correlation coefficient (RP) and standard deviation (SEP) of prediction sets are 0.900 3 and 4.101 2. The relevance reaches a higher level and the standard deviation is small. Therefore, the experiment shows that combining infrared thermal imaging technology with GRNN to study germination rate of rice seed is feasible. The model has a higher accuracy in terms of rapid determination of the germination rate of rice seed.

[Genetic analysis on aging-resistant in rice seed].

Relative germination percentage, relative germination rate, and relative germination index were studied using P1, P2, and 350 lines of a RIL population from the japonica rice (Oryza sativa L. japonica) cross of Bing 8979/C Bao. By means of mixed major gene plus polygene inheritance models, genetic analysis showed that relative germination percentage, relative germination rate, and relative germination index were controlled by two major genes plus polygenes. Both major genes and polygenes showed additive-epistatic effects. The additive effects were larger than epistatic effects in the two major genes. The heritability of major genes was larger than that of polygenes in all the three traits, indicating that the three traits investigated in the present study were mainly governed by major genes.

[QTL analysis of the uppermost internode length in rice under different growing environments].

A mapping population of 98 backcross inbred lines ( BC1F12 and BC1F13 ), derived from a backcross of Nipponbare (japonica) / Kasalath (indica) // Nipponbare by the single seed descent methods, was employed to map quantitative trait loci (QTL) of the uppermost internode length in rice under different growing environments by composite interval mapping method. The results showed that 13 QTLs relating to the uppermost internode length were detected under three differ- ent growing environments, and these QTLs were located on chromosome 1, 2, 3, 5, 6, 8, 10 and 11, explained 3.97 -15.21% of observed phenotypic variance, respectively. The locus of qUIL-6 was detected in three growing environments, and qUIL-1a, qUIL-3a, qUIL-3b and qUIL-10a were detected in two growing environments, indicating that these QTLs were stable and little affected by the environment.

[The correlation between panicle angle and rice quality and genetic analysis on rice quality in japonica rice (Oryza sativa L.)].

Linear correlation between panicle angle and 10 quality traits were studied by using P1, P2 and 349 lines of the RIL population in a cross of Bing 8979(erect panicle)/C bao (curve panicle) in japonica rice. We found that, there were no linear correlation between panicle angle and brown rice rate , head rice rate, chalky grain rate, chalkiness area, gelatinization temperature, gel consistency and apparent amylose content. The correlation coefficients between panicle angle and milled rice rate, between panicle angle and grain length, and between panicle angle and grain length/width ratio were 0.124*, 0.470** and 0.241** , respectively. By using major gene-polygene mixed inheritance models, genetic analyses showed that brown rice rate, milled rice rate and apparent amylose content were controlled by two major genes plus polygenes. The two major genes expressed additive effect and additive x additive effects. Head rice rate, grain length, grain length/width ratio and gel consistency were controlled by two major genes with additive-epistatic effects plus polygenes. Chalky grain rate, chalkiness area and gelatinization temperature were controlled by three major genes with additive-epistatic effects plus polygenes. Brown rice rate, milled rice rate, head rice rate, chalky grain rate, chalkiness area and gelatinization temperature traits were mainly governed by major genes. Grain length, grain length/width ratio, gel consistency and apparent amylose content traits were mainly governed by polygenes.