## 3 Results and Discussion
The calibration process of the lidar consists of developing an algorithm for synchronizing damage with fixing the force exerted by the electronic potato \"Electronic Tuber Log\" and recording the fluorescence spectra of the potato tuber sample under study. The resulting nomographic chart indicates that the change in wavelength pumping intensity in the wavelength ranges from 600 to 750 nm corresponding to a damage value of 2 to 24 N, ranging from a minimum of 80 (at the full damage range of 2 to 24 maximum) to a maximum pumping intensity of 443, corresponds to a fluorescence spectrum with a clear separation of their boundaries. The maximum interval of the difference in fluorescence spectra intensity is observed in the wavelength ranges between 625 and 700 nm, with a clear conditional separation of the force impact from 2 to 24 N. Thus, it is necessary to notice that only the potato tubers at the maximum force impact up to 12 N are suitable for stable storage after harvesting and postharvest handling. A further increase in mechanical effect in the studied range excludes any possibility of storing such potato tubers as the damaged tubers provoke the growth of various pathogens, including their spread on marketable ones. The maximum value of 872 units of fluorescence spectrum intensity in the wavelength range of 625-700 nm was observed under the force impact of 24 N, followed by a reduction in spectrum intensity in the average value of 2.5% for each interval of the lesion range within the whole wavelength spectrum, with the minimum value of spectrum intensity of 800 units, corresponding to the wavelength of 700 nm and the force impact of 2 N.  
Research results by [PERSON] and [PERSON] [31] indicates that the susceptibility of potato tubers to mechanical damage of tubers, as measured by the criterion of skin hardness, determined by a penetrometer, is not a satisfactory alternative to the damage index for determining the ratio of susceptibility to mechanical damage of tubers of different varieties. Many exceptions to the general trend of an inverse correlation between skin hardness and damage within have not been observed. The ratio of surface-hardness-to-hardness at a depth of 2 mm provides only a very rough guide to cultivar susceptibility to damage, and therefore this method of assessing damage to potato tubers is unsatisfactory; in this regard, the use of the above method for rapid assessment of damage to potato tubers  
Figure 6:1 Nomographic chart of fluorescence spectra of the damaged potato tubers at wavelengths of 600, 650, and 700 nm. before storage is promising for further expansion of research in terms of determining the correlation between damage and spectra of other potato varieties. [PERSON] et al. managed to extract potato properties by means of image processing. Their goal was to find out the quality and to evaluate the physical properties of potatoes using artificial neural networks i.e., ANNs, a method which was named blurred ARTMAP because of its stability and convergence speed outperformed the other models. They suggested a few algorithms to determine potato defects like greening, scabs and cracks, which could be efficiently used to grade potatoes of different qualities [32]. [PERSON] et al. [33] performed the recognition of crack defects and rhizotoniosis based on the recognition that the total precision of potato sorting was 97.67% by tuber skin color using machine vision (lighting chamber, lighting source, CCD camera, personal computer), however, this method is unsuitable at the stages of laying potatoes for storage, since the change in color occurs 3-5 days after the damage and the laying of the damaged tubers for storage leads to the processes of their decay in storage and the processes of infection of healthy tubers.