Automated turf testing apparatus and system for using same

1 . A computer-implemented method for inspecting a ground surface, the method comprising:

capturing, via at least one camera, an optical image of the ground surface, wherein the ground surface includes at least one of: grass, artificial turf, infill, and dirt, and the optical image includes at least one of a photographic image and a video image;

capturing, via at least one laser, a three-dimensional (“3D”) depth scan of the ground surface; and

via a computing processor, in response to executable instructions:

electronically combining the optical image of the ground surface and the 3D depth scan of the ground surface;

sampling or recording one or more color, one or more depth, or a combination of the one or more color and the one or more depth, in a portion of or all of the optical image, the 3D depth scan, or a combination of the optical image and the 3D depth scan;

creating a mask using the sampling or recording of the one or more color, the one or more depth, or the combination of the one or more color and the one or more depth, in the portion or all of the optical image, the 3D depth scan, or a combination of the optical image and the 3D depth scan;

using the mask to distinguish (a) fiber of at least one of the grass or the artificial turf from (b) at least one of the infill or the dirt; and

determining, using the optical image or the 3D depth scan, one or more lay direction of the grass or the artificial turf, in both a two-dimensional optical space and a 3D depth space.

2 . The computer-implemented method of claim 1 , further comprising characterizing a geometry of the fiber of the artificial turf or a morphology of the fiber of the grass to measure or analyze at least one of usage, wear, and tear, of the artificial turf or the grass.

3 . The computer-implemented method of claim 1 , wherein the measurement or the analysis are replicable and reproducible.

4 . The computer implemented method of claim 1 , wherein the measurement or the analysis are to differentiate between the grass and the dirt.

5 . The computer-implemented method of claim 1 , wherein the measurement or the analysis are to at least one of:

differentiate green grass from dormant grass, thatch, or a plant material of a different color from the grass;

differentiate grass blades from another morphological structure chosen from one or more of: stolon, rhizome, crown, seedhead, or a morphological structure having at least one of a different shape or color than the grass blades; or

differentiate the artificial turf from the infill.

6 . The computer-implemented method of claim 5 , wherein the infill is chosen from at least one of: rubber crumb, polymeric infill, sand, organic particulate material, or inorganic particulate material.

7 . The computer-implemented method of claim 1 , wherein the at least one camera is a high-speed camera.

8 . The computer-implemented method of claim 7 , wherein the high-speed camera is capable of taking over 300 frames per second.

9 . The computer-implemented method of claim 1 , further comprising at least one of machine learning visual recognition or data synchronization.

10 . The computer-implemented method of claim 1 , further comprising providing for at least one of: identifying foreign objects in or on the ground surface, determining safety for playing a sport on the ground surface, performing one or more ground surface evenness test, or determining grass or artificial turf density.

11 . The computer-implemented method of claim 1 , further comprising providing a user interface capable of generating or presenting at least one of data, data analysis, or data interpretation.

12 . The computer-implemented method of claim 1 , wherein the computer processor is located on a same apparatus also including the at least one camera and the at least one laser, or wherein the computer processor is located remote from an apparatus including the at least camera and the at least one laser, and wherein the computer processor provides a scoring or ranking of the ground surface.

13 . The computer-implemented method of claim 1 , wherein two or more of the at least one laser are different colors.

14 . The computer-implemented method of claim 1 , wherein a first laser of the at least one laser is a first color and a second laser of the at least one laser is a second color.

15 . The computer-implemented method of claim 1 , wherein a plurality 3D depth scans are partially or completely overlapping or overlaid relative to one another.

16 . The computer-implemented method of claim 1 , wherein:

the at least one laser is reflected off at least one mirror and oriented orthogonally or substantially orthogonally to the at least one camera;

a first laser of the at least one laser is oriented orthogonal to a second laser of the at least one laser;

a first laser and a second laser of the at least one laser are oriented orthogonal to one another, providing for scanning from a plurality of angles, which accounts for directionality problems or abnormalities related to the ground surface; and/or

a first laser and a second laser of the at least one laser are oriented orthogonal to one another, and wherein the orthogonal orientation at least one of: (a) cancels out shadows in a depth map, and (b) senses behind taller features that block one or more beam from the first laser or the second laser scanning from other angles.

17 . The computer-implemented method of claim 1 , further comprising providing one or more geared step motor to at least one of:

direct the at least one laser across the ground surface by at least one of aligning or directing one or more steering mirror for the at least one laser;

direct one or more beams from the at least one laser to swipe or scan across the ground surface, which generates a scan and depth map; or

direct one or more beams from the at least one laser to swipe or scan across the ground surface, which generates the 3D depth scan.

18 . The computer-implemented method of claim 1 , further comprising providing a first depth map of the fiber of the at least one of the grass or the artificial turf, and a second depth map of the at least one of the infill or the dirt, which are used to at least one of:

extract data about evenness of any one or more of the grass, the artificial turf, the infill, or the dirt, together or compared against one another;

extract data about coverage of the at least one of the grass, the artificial turf, the infill, or the dirt; or

extract data comparing how much infill or dirt is exposed as compared to the grass or the artificial turf.

19 . The computer-implemented method of claim 1 , further comprising providing a depth map of the fiber of the at least one of the grass or the artificial turf, which is used to extract data regarding geographical characteristics, morphological characteristics, or both, of at least one of the fiber of the artificial turf, the fiber of the grass, or a fiber of a grass blade.

20 . The computer-implemented method of claim 1 , further comprising using at least one of the optical image and the 3D depth scan, to provide a statistical sampling of a three-dimensional orientation at least one of the grass and the artificial turf.

21 . The computer-implemented method of claim 1 , further comprising at least one of:

(a) compiling test results and displaying them via a user interface;

(b) comparing test results against hard-coded or server-based baseline data to score the test results against;

(c) retrieving historical results from tests and comparing the historical results with baseline hard-coded data, and/or comparing the historical results with new test results;

(d) evaluating and scoring geographical consistency of the ground surface by registering more than one test with one or more location using a Global Positioning System and analyzing test results from multiple locations using at least one of correlation, coefficient of variation, standard error, or standard deviation, to assess variability;

(e) flagging or recommending intervention if the ground surface may be dangerous or of poor quality; or

(f) one or more of collecting, registering, synchronizing, retrieving, or analyzing metadata related to the ground surface.

22 . The computer-implemented method of claim 1 , further comprising using the mask to isolate two components of the ground surface for measuring or determining:

a distribution of artificial turf height from a ground surface, height of the artificial turf over infill, or combinations thereof;

a variation of detected heights;

a distribution of categories represented in the optical image;

existence of one or more patch of exposed infill or dirt;

tape width, tape wear, or combinations thereof, using blob detection or filtered line detection;

artificial turf lay using orientation of detected blobs and a perpendicular direction to height gradient;

color distribution of the grass;

detection of grass color;

detection of paint, paint color, or both, on any one or more of the grass, the artificial turf, the infill, or the dirt;

distribution clusters of color in the optical image; and/or

development of at least one of grass species or seasonality benchmarks, including at least one of change in color, morphology, or quality decline due to disease or pests.

23 . The computer-implemented method of claim 1 , further comprising presenting one or more surface feature to a user as at least one of a single metric, a distribution, an average, or distributions over time.

24 . A computer-implemented method for inspecting a ground surface, the method comprising:

capturing, via at least one camera, an optical image of the ground surface, wherein the ground surface includes at least one of: grass, artificial turf, infill, and dirt, and the optical image includes at least one of a photographic image and a video image;

capturing, via at least one laser, a three-dimensional (“3D”) depth scan of the ground surface; and

via a computing processor, in response to executable instructions:

electronically combining the optical image of the ground surface and the 3D depth scan of the ground surface;

sampling or recording one or more color, one or more depth, or a combination of the one or more color and the one or more depth, in a portion of or all of the optical image, the 3D depth scan, or a combination of the optical image and the 3D depth scan;

creating a mask using the sampling or recording of the one or more color, the one or more depth, or the combination of the one or more color and the one or more depth, in the portion or all of the optical image, the 3D depth scan, or a combination of the optical image and the 3D depth scan;

using the mask to distinguish (a) fiber of at least one of the grass or the artificial turf from (b) at least one of the infill or the dirt;

electronically measuring or analyzing, or both electronically measuring and analyzing, at least one of the grass, the artificial turf, the infill, and the dirt; and

wherein the one or more color comprises two different colors, which can be automatically selected based on dispersion from the at least one laser depending on at least one of ground surface color and ground surface sheen.