Method for predicting body mass of herd animal, computer-readable storage media, system for predicting body mass of herd

A non-transitory computer readable medium, comprising a set of instructions that, when executed by a processor, causes the processor to perform operations comprising:

• collecting data from the livestock environment, including:
• capturing and storing videos of the livestock environment, wherein the videos are RGB-D videos of the top view of the animal;
• weighing the animal individually;
• detecting and selecting the animal, including:
• extracting RGB-D depth images from stored video;
• wherein each RGB-D depth image extracted from the video consists of points that are represented by Cartesian coordinates, and
• wherein the points of the RGB-D depth image are associated with the colors of the RGB standard;
• and
• forming a color map for each RGB-D depth image;
• identifying and separating the animal from other elements included in the RGB-D depth image extracted from the video;
• determining geometric characteristics of each RGB-D depth image including the top view of the animal;
• wherein geometric characteristics include distance between points, area and volume;
• wherein the distance between points includes any of:
• the greatest or the average of the ‘n’ greatest longitudinal distances from the top view of the animal (Ln);
• the greatest or average of the ‘n’ greatest transverse distances from the top view of the animal (Tn);
• and the greatest or average of the ‘n’ greatest vertical distances (Hn) between the top view of the animal and the ground;
• wherein the ground is represented by points of zero height;
• and
• obtaining the prediction of body mass of herd.

A method for predicting body mass of herd animal, comprising the steps of:

• collecting data from the livestock environment, including:
• capturing and storing videos of the livestock environment, wherein the videos are RGB-D videos of the top view of the animal;
• weighing the animal individually;
• detecting and selecting the animal, including:
• extracting RGB-D depth images from stored video;
• wherein each RGB-D depth image extracted from the video consists of points that are represented by Cartesian coordinates, and
• wherein the points of the RGB-D depth image are associated with the colors of the RGB standard;
• and
• forming a color map for each RGB-D depth image;
• identifying and separating the animal from other elements included in the RGB-D depth image extracted from the video;
• determining geometric characteristics of each RGB-D depth image including the top view of the animal;
• wherein geometric characteristics include distance between points, area and volume;
• wherein the step of determining geometric characteristics of each RGB-D depth image including the top view of the animal further comprises applying a triangulation method and a contour identification method to the RGB-D depth image including the top view of the animal;
• and obtaining the area of the three-dimensional surface (A3D), the area of the two-dimensional projection on the plane of the top view of the animal (A2D), its perimeter (P) and volume of the three-dimensional surface (V);
• and
• obtaining the prediction of body mass of herd.