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		题名	作者	年代	出处	被引量
	1	Rapid Identification of Rice Samples Using an Electronic Nose显示文摘Four rice samples of long grain type were tested using an electronic nose (Cyranose-320).Samples of 5 g of each variety ofrice were placed individually in vials and were analyzed with the electronic nose unit consisting of 32 polymer sensors.TheCyranose-320 was able to differentiate between varieties of rice.The chemical composition of the rice odors for differentiatingrice samples needs to be investigated.The optimum parameter settings should be considered during the Cyranose-320 trainingprocess especially for multiple samples,which are helpful for obtaining an accurate training model to improve identificationcapability.Further,it is necessary to investigate the E-nose sensor selection for obtaining better classification accuracy.A re-duced number of sensors could potentially shorten the data processing time,and could be used to establish an application pro-cedure and reduce the cost for a specific electronic nose.Further research is needed for developing analytical procedures thatadapt the Cyranose-320 as a tool for testing rice quality.	John Westbrook W.C.Hoffmann R.E.Lacey	2009	Journal of Bionic Engineering2009，6，3：	28
	2	Analysis of vegetation indices derived from aerial multispectral and ground hyperspectral data显示文摘Aerial multispectral images are a good source of crop,soil,and ground coverage information.Spectral reflectance indices provide a useful tool for monitoring crop growing status.A series of aerial images were obtained by an airborne MS4100 multispectral imaging system on the cotton and soybean field.Ground hyperspectral data were acquired with a ground-based integration system at the same time.The Normalized Difference Vegetative Index(NDVI),Simple Ratio(SR),and Soil Adjusted Vegetation Index(SAVI)calculated from both systems were analyzed and compared.The information derived from aerial multispectral images has shown the potential to monitor the general growth status of crop field.The vegetation indices derived from both systems were significantly different(p-value was 0.073 atα=0.1 level)at the early growing stage of crops.The correlation coefficients of the image NDVI and ground NDVI were 0.3029 for soybean field and 0.338 for cotton field.SAVI and SR were not correlated.	Huihui Zhang Yubin Lan Ronald Lacey W.C.Hoffmann Yanbo Huang	2009	International Journal of Agricultural and Biological Engineering2009，2，3：	2

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