## 6 Conclusions  
In this paper, a method of color discrimination based on sample sensitivity to light wavelength is proposed, and a laser detection system is designed to realize the color discrimination. For the color discrimination of polycrystalline silicon cells, according to the measured reflection spectra of a large number of polycrystalline silicon cells and the statistical calculation of the measurement data, the most sensitive wavelength, 434 nm, and the least sensitive wavelength, 645 nm, of polycrystalline silicon cells is obtained.  
Then a laser detection system was built to measure the samples. This system consists of two lasers, optical shutters, collimating beam expanding systems, an optical coaxial system, sample platform, collecting lens, and optical power meter or optical sensor. The wavelengths of the two lasers are respectively 434 nm (the most sensitive wavelength of polycrystalline silicon cell) and 645 nm (the least sensitive wavelength of polycrystalline silicon cell). Two laser beams of different wavelengths are beamed coaxially through the optical coaxial system onto a polycrystalline silicon cell and are reflected or scattered. The reflected or scattered light is collected through a lens with a high number aperture and received separately by the optical power meter.  
Then, the color value of the polycrystalline silicon cell in this system is characterized by the ratio of light intensity data received. Finally, the system measured a large number of previous polycrystalline silicon cells to form the different color categories intervals of polycrystalline silicon cell of this system in the computer database. When a new polycrystalline silicon cell is measured, the color discrimination system can automatically classify the new polycrystalline silicon cell to a certain color category according to the measured color value of the cell and the color categories intervals of polycrystalline silicon cell, to achieve color discrimination.  
Described in this paper, the method and its corresponding system can better solve the problem of complex circumstance of color discrimination. The normalized color value improves the accuracy of the color value, which is not affected by the external light source and the unstable power of the detection laser. The irregularity of sample surface is solved by the coincident axis irradiation of laser. The acquisition of sensitive wavelength and insensitive wavelength makes the similar color can be discrimination problem can be accurately resolved.  
The method proposed in this study is a laser detection system based on the spectral information of samples. Similarly, in indoor decoration, the wooden floor boards of the same house often need to be of the same color before being laid. Thus, colour discrimination is necessary between, say, shallow yellow and deep yellow. This problem also can rely on the method proposed in this study for resolution. However, it is necessary to change the wavelength of the light for measurement and for reference, the size of the laser spot, the numerical aperture of the collecting lens, and the spectral range of the optical power meter. For the long floor board, the two-dimensional electronic translation unit can be installed on the back side of the sample platform. The color information can be obtained by scanning the floor board, and the color classification can be carried out according to the above method. Similarly, in the sorting of coffee beans, seeds, and other materials, the quality of coffee beans and seeds is judged by the difference in color. The color classification corresponding to their quality can also be realized by relying on the method proposed in this study, but it also needs to change the wavelength of the light, the size of the laser spot, the numerical aperture of the collecting lens, and the spectral range of the optical power meter.  
Therefore, how to apply the method proposed in this paper to color discrimination and color recognition in agriculture, industry, and daily life remain problems to be further studied.