No abstract

Spectral imaging is a promising technique for detecting the quality of rice seeds. However, the high cost of the system has limited it to more practical applications. The study was aimed to develop a low-cost narrow band multispectral imaging system for detecting rice false smut (RFS) in rice seeds. Two different cultivars of rice seeds were artificially inoculated with RFS. Results have demonstrated that spectral features at 460, 520, 660, 740, 850, and 940 nm were well linked to the RFS. It achieved an overall accuracy of 98.7% with a false negative rate of 3.2% for Zheliang, and 91.4% with 6.7% for Xiushui, respectively, using the least squares-support vector machine. Moreover, the robustness of the model was validated through transferring the model of Zheliang to Xiushui with the overall accuracy of 90.3% and false negative rate of 7.8%. These results demonstrate the feasibility of the developed system for RFS identification with a low detecting cost.

Prediction of car ownership has direct reference significance for the development of urban transportation and construction of urban roads. By analyzing the impact factors of urban auto possession, this paper first analyzes 8 indicators such as urban population, GDP, road passenger traffic and so on determined by some references, then establish BP neural network model to predict the vehicles possession in Hunan Province from 2006 to 2008. The figures of prediction is 989,300, 1,221,800 and 1,370,300 respectively in 2006, 2007 and 2008, which is very close to the real ownership of 946,400,1,217,200 and 1,426,700 respectively. It shows the prediction is very accurate. This suggests that the BP neural network has very strong learning and generalization ability and can be employed in prediction of vehicle possession effectively. The prediction of car ownership, as a foundational work for transportation planning,has direct reference significance on the development of urban traffic,its control and management and construction of urban road, etc.Early in 1940s this research has been started in foreign countries[1]. Many different models of prediction of car ownership have been developed.Many of them are developed mainly based on the factors such as urban economy, population network capacity, the land utilization and parking facilities.In China there are also some researches on this issue. They predicate the car ownership mainly by time series prediction, regression analysis and fractal theory and entropy method [2~6].However, these methods do not comprehensively describe the complex relationship between car ownership and other factors. The author of this paper chooses some car ownership-related factors and employ principal component method to analyze to obtain the main factors, then tries to find the relationship between BP neural networks and car ownership according to these factors so as to predict the car ownership in Hunan Province form 2006 to 2008, which will be greatly significant to the development of urban transportation, management and construction.

Salt stress is one of the abiotic factors that causes adverse effects in plants and there is an urgent need to detect salt stress in plants as early as possible. Multicolor fluorescence imaging, as a powerful tool in plant phenotyping, can provide information about primary and secondary metabolism in plants to detect the responses of the plants exposed to stress in the early stage. The purpose of this study was to evaluate the potential of multicolor fluorescence imaging’s application in the early detection of salt stress in plants. In this study, the measurements were conducted on Arabidopsis and the multicolor fluorescence images were acquired at 440, 520, 690, and 740 nm with a self-developed imaging system consisting of a UV light-emitting diode (LED) panel for an excitation at 365 nm, a charge coupled device (CCD) camera, interference filters, and a computer. We developed a classification method using the imaging analysis of multicolor fluorescence based on principal component analysis (PCA) and a support vector machine (SVM). The results showed that the four principal fluorescence feature combinations were the ideal indicators as the inputs of the SVM model, and the classification accuracies of the control and salt-stress treatment at 5 days and 9 days were 92.65% and 98.53%, respectively. The results indicated that multicolor fluorescence imaging combined with PCA and SVM could act as a tool for early detection in salt-stressed plants.