In order to study the effect of the physical properties of tailings on the concentration of tailing slurry underflow, the median particle size, control particle size, and density of the tailings of 9 typical mines were measured. Besides, static settlement test and dynamic thickening test of the tailing slurry of these 9 mines were separately conducted. The test results show that the underflow concentration of the tailing slurry is unrelated to the tailing density but is related to the tailing particle size. The finer the tailing particle size, the lower the underflow concentration. Through the nondimensional regression analysis, 3 relation equations were obtained: number 1 relation equation between the median particle size of tailings and the underflow concentration in the static settlement test, number 2 relation equation between the control particle size of tailings and the change in concentration in the static settlement test, number 3 relation equation between the underflow concentration in the static settlement test and the underflow concentration in the dynamic thickening test under different overflow water solid content conditions. Comparing the calculation results with the test results, it is found that the relative error between the predicted value and the test value does not exceed 6.1%, which has proved that the relation equations derived are convenient and feasible. The underflow concentration of the deep cone thickener can be predicted by merely measuring the particle size of the tailings, which means that the relation equations are highly recommended for wider use and application.

Highlights A method of locating sugarcane seed bud based on anisotropy transformation is proposed.Using computer binocular vision technology, the location of the sugarcane seed bud was determined by edge feature matching of the sugarcane seed bud.There are few methods to study the automatic location of sugarcane seed buds, and our research provides a new research idea.Abstract. Sugarcane is a major economic crop in China, but the degree of mechanization in sugarcane cultivation is low. To improve the economic benefit of sugarcane planting, promoting the use of mechanization in sugarcane planting is necessary. Currently, the sugarcane planted using mechanization has a low survival rate and the mechanization efficiency is low because the existing sugarcane precutting machine fails to address the problem of damaging seed buds. This study proposed a sugarcane bud localization method based on computer binocular vision technology. The sugarcane stem segment positions can be determined by the grayscale horizontal projection after preprocessing the sugarcane image based on color and grayscale features. Then, the bud area can be intercepted according to the positional relationship between the seed bud and the stem node, and the planar position of the seed bud will be determined by using the color space conversion and the gray vertical projection. Finally, the anisotropic scaling transformation is used to match the seed-bud area and restore the spatial coordinates of the seed bud, and the spatial position of the seed bud can be determined. The image pyramid acceleration matching process is adopted, which can make the method more suitable for real-time applications. The experimental results show that the accuracy of seed-bud matching based on the anisotropic scaling transformation is 98%, which provides a basis for research on the anti-injury germ system in the automatic planting process of sugarcane. Keywords: Anisotropic scaling, Binocular vision, Image pyramid, Mechanization planting of sugarcane, Seed bud location.