Greenhouse production systems are increasingly complex, necessitating a data-driven approach with robust, intelligent sensors. Although there is a clear advantage for growers to be able to monitor the physiological status of the crop, current practices mostly involve cumbersome, expensive, and slow laboratory measurements. It is shown that visible and near-infrared imaging spectroscopy allows for the rapid and non-destructive assessment of the concentrations of sugars, starch, pigments, various nutrients, and dry matter in the leaves and fruits of tomato plants. A tailored feature selection algorithm also shows the feasibility of using as few as 8 bands with spectral cameras. This study validates imaging spectroscopy as a rapid tool for assessing crop status and fruit quality in greenhouse horticulture.
Spectral image sensors provide images with a large number of contiguous spectral channels per pixel. Visualization of these huge data sets is not a straightforward issue. There are three principal ways in which spectral data can be presented; as spectra, as image and in feature space. This paper describes several visualization methods and their suitability in the different steps in the research cycle. Combinations of the three presentation methods and dynamic interaction between them, adds significant to the usability. Examples of some software implementations are given. Also the application of volume visualization methods to display spectral images is shown to be valuable.
Spectral image sensors provide images with a large umber of contiguous spectral channels per pixel. This paper describes the calibration of spectrograph based spectral imaging systems. The relation between pixel position and measured wavelength was determined using three different wavelength calibration sources. Results indicate that for spectral calibration a source with very small peaks,such as a HgAr source, is preferred to arrow band filters. A second order polynomial model gives a better fit than a linear model for the pixel to wavelength mapping. The signal to noise ratio (SNR)is determined per wavelength. In the blue part of the spectrum,the SNR was lower than in the green and red part.This is due to a decreased quantum efficiency of the CCD,a smaller transmission coefficient of the spectrograph,as well as poor performance of the illuminant. Increasing the amount of blue light,using additional Fluorescent tube with special coating increased the SNR considerably. Furthermore, the spatial and spectral resolution of the system are determined.These can be used to choose appropriate binning factors to decrease the image size without losing information.
Comparison in the RGB domain is not suitable for precise color matching, due to the strong dependency of this domain on factors like spectral power distribution of the light source and object geometry. We have studied the use of multispectral or hyperspectral images for color matching, since it can be proven that hyperspectral images can be made independent of the light source and object geometry. Hyperspectral images have the disadvantages that they are large compared to regular RGB-imags, which makes it infeasible to use them for image matching across the Internet. For red roses, it is possible to reduce the large number of bands of the spectral images to only three bands, the same numbers of an RGB-image, using Principal Component Analysis, while maintaining 99 percent of the original variation. The obtained PCA-images of the roses can be matched using for example histogram cross correlation. From the principal coordinates plot, obtained from the histogram similarity matrices of twenty images of red roses, the discriminating power seems to be better for normalized spectral images than for color constant spectral images and RGB-images, the latter being recorded under highly optimized standard conditions.
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