| With the development of social economy and the enhancement of living standards, principal contradiction between rice supply and demand was transformed from insufficient quantity to unfavourable quality. Therefore, it was significant to establish rapid rice quality detection method for the purpose of rice breeding, food processing and trade in agriculture.Chalkiness, amylose content and aging of rice have key influence on edible quality of rice. Therefore, in this paper, rapid detection methods for these three characters were studied based on uncertainty artificial intelligence and Chemometrics theory, using digital image processing technology, spectral analysis technology, wavelet analysis and pattern recognition technology.In order to improve identification accuracy and adaptability for chalkiness in computer vision system, the main content about chalkiness rapid detection method were as follows:(1) Image acquisition environment under visible light was established. A computer vision system for image acquisition was set up, analyzing such factors on rice image quality as light source, voltage, and background, and finally the best environmental condition for rice image acquisition was determined. Under transmission light, the best voltage was 6.4V, the best light was LED and the background color was light blue. Under reflected light, the best voltage was 6.0V, the best light is LED and the background color was deep blue.(2) Histogram of the paddy rice image acquired under the transmitted light and the reflected light was analyzed, the appropriate image denoising method and image segmentation algorithm were studied. The result of analyzing histogram of different color space showed that the gray scale distribution of image collected under the reflected light was suitable for chalkiness recognition. Weighted average value filter template was designed in order to not only eliminate the noise but also protect the boundary. The image segmentation algorithm-ostu method was discussed and the rice image rectangular region size was determined based on it.(3) The adaptive chalkiness recognition algorithm was studied. This method was introduced on the basis of the uncertainty artificial intelligence theory and the cloud model. In this method, chalkiness and non-chalkiness were defined as two qualitative concepts, and then they were expressed by an asymmetrical cloud and a symmetrical cloud separately. Chalkiness cloud and non-chalkiness cloud were described by two groups of digital characteristics. Artificial estimation method, the fixed threshold method and the cloud classification method were used in chalkiness area detection under the same voltage value to test the accuracy of cloud classification method. The result showed that the cloud classification's precision was higher than artificial estimation method, because the average value of deviation between cloud classification and fixed threshold method (i.e. accurate value) was 0.97, while average value of deviation between artificial estimation method and fixed threshold method(i.e. accurate value) was 1.93. Artificial estimation method and cloud classification method were used in chalkiness area detection under the different voltage value to test the adaptability of cloud classification method. The result showed that the cloud classification method's adaptability was better than the artificial estimation method.In order to establish the near-infrared spectrum quantitative analysis model with good stability and high prediction precision for paddy rice amylose content, the content mainly studied were as follows:(1) The influence of spectrum collecting parameter to the paddy rice near-infrared spectral response characteristic was studied. The spectrums were collected for paddy rice with the same amylose content under the different parameter. The best collecting parameter was determined according to the result of spectrum statistical analysis. The scanning times was 64, the resolution was 8cm"1, and the indoor temperature was 15℃(2) Abnormal spectrum removing method and the spectrum pretreatment method were studied for rice spectrums. In order to optimize the calibration set of sample and enhance the precision of the model,18 abnormal spectrums which were produced from subjective and objective factors were removed from calibration sample set based on mahalanobis distance and forecast concentration residual criterion. The factors such as baseline drift and shift, instrument's random noise, the stray light had disturbance to the spectrum. In order to eliminate disturbance and improve signal-to-noise ratio, many kinds of methods were used to process the paddy rice spectrums and their influence to model result were compared. The first derivative combined with the SG convolution smoothing method was determined through comparison of model evaluation index.(3)The influence of quantitative analysis method to the model effect was studied. Multiple linear stepwise regression, principal component regression and partial least square regression were used to develop models on spectrums processed by first derivative combined with the SG convolution smoothing method. The model developed by partial least square regression resulted in the best stability, the highest correlation coefficient of the predicted value and the actual value, and the smallest root mean square error of prediction. The correlation coefficient of the predicted value and the actual value was 98.96%, the root mean square error of calibration was 0.62, the root mean square error of prediction was 1.19, and the root mean square error of cross validation was 1.58.In order to study the potential of near-infrared reflectance spectroscopy for detecting aging rice, the main content about aging rice rapid detection method are as follows:(1) The near-infrared spectral response characteristic of the aging rice and the non-aging rice were studied. The influence of different spectrum pretreatment method to the cluster effect was studied using the principal components analysis. The results showed that it was feasible to use paddy rice's near-infrared spectrums to recognize aging rice qualitatively. Original spectrums were selected to participate in the model according to the cluster principle that the distance in class was the smallest and the distance between classes was the biggest.(2) The effective characteristic extraction method was studied for spectrums of rice. Wavelet analysis could not only withdraw the sensitive signature information of spectrum, but also reduce the dimension of data effectively. Therefore, wavelet analysis combined with support vector machine method provided an effective pattern recognition method.77 wavelet coefficients which obtained from the db6 wavelet transformation were taken as the input of the support vector machine model. When the decomposition size is 5, the number of data points of each spectrum was reduced from 2127 to 77.(3) The parameter choice of support vector machine model was studied. Firstly, linear function was took as the nuclear function without internal parameter, and the model was developed with changing value of penalty factor. The results showed that the best value of error punishment factor C was 1000. Secondly, support vector machine model was developed using different nuclear function as well as the nuclear function's internal parameter. The experiment results showed that the identification accuracy was 98.45%when the nuclear function is the redial basis function and its parameterγis 16.
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