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371 results about "Spectral sensitivity" patented technology

Spectral sensitivity is the relative efficiency of detection, of light or other signal, as a function of the frequency or wavelength of the signal. In visual neuroscience, spectral sensitivity is used to describe the different characteristics of the photopigments in the rod cells and cone cells in the retina of the eye. It is known that the rod cells are more suited to scotopic vision and cone cells to photopic vision, and that they differ in their sensitivity to different wavelengths of light. It has been established that the maximum spectral sensitivity of the human eye under daylight conditions is at a wavelength of 555 nm, while at night the peak shifts to 507 nm.

Nondestructive detection method for physiological index of plant leaf

The invention discloses a nondestructive testing method used for testing the physiological indexes of plant leaves on the basis of invisible-near infrared spectrum, which can carry out the quick and multi-parameter testing on the content of compositions such as chlorophyll, nitrogen, lutein, water and the like simultaneously. The method carries out spectrum collection on calibration samples, subsequently preprocesses the spectrum data, preferably selects the waveband, establishes the calibration model between the spectrum value and the standard value of the content of plant component, and collects the spectrums of the unknown samples; after the spectrum data is pre-processed, the selected waveband data are substituted in the calibration model so as to predict the content of the component to be measured; the technical proposal of the invention adopts full-spectrum information; the measured parameters have strong extensibility and the prediction precision and the model adaptability of the calibration model are improved; the trans-reflective measurement type adopted by the method adopts the spectrum sensitiveness and has stronger adaptability on the leaf type; and the improved wavelet analysis method can simultaneously eliminate the noise of the leaf spectrum data and carries out benchmark line calibration pre-processing on the leaf spectrum and can effectively improve the prediction precision.
Owner:BEIHANG UNIV
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