Abstract: Aspects of the subject disclosure may include, for example, obtaining video data from a single monocular camera, wherein the video data comprises a plurality of frames, wherein the camera is attached to a mobile robot that is travelling along a lane defined by a row of crops, wherein the row of crops comprises a first plant stem, and wherein the plurality of frames include a depiction of the first plant stem; obtaining robot velocity data from encoder(s), wherein the encoder(s) are attached to the robot; performing foreground extraction on each of the plurality of frames of the video data, wherein the foreground extraction results in a plurality of foreground images; and determining, based upon the plurality of foreground images and based upon the robot velocity data, an estimated width of the first plant stem. Additional embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining video data from a single monocular camera, wherein the video data comprises a plurality of frames, wherein the camera is attached to a mobile robot that is travelling along a lane defined by a row of crops, wherein the row of crops comprises a first plant stem, and wherein the plurality of frames include a depiction of the first plant stem; obtaining robot velocity data from encoder(s), wherein the encoder(s) are attached to the robot; performing foreground extraction on each of the plurality of frames of the video data, wherein the foreground extraction results in a plurality of foreground images; and determining, based upon the plurality of foreground images and based upon the robot velocity data, an estimated width of the first plant stem. Additional embodiments are disclosed.

Abstract: Provided is a process including: obtaining, with a computer system, a data set having labeled members with labels designating corresponding members as belonging to corresponding classes; training, with the computer system, a machine learning model having deterministic layers and a parallel set of conditional layers each corresponding to a different class among the corresponding classes, wherein training includes adjusting parameters of the machine learning model according to an objective function that is differentiable; and storing, with the computer system, the trained machine learning model in memory.

Abstract: Provided is a process including: obtaining, with a computer system, with a stochastic layer of a multi-layer neural network, inputs to the stochastic layer from, wherein the multi-layer neural network comprises both deterministic layers and the stochastic layer, and the stochastic layer comprises a plurality of parameters that vary stochastically according to respective probability distributions; determining values of the plurality of parameters by randomly sampling from the statistical distributions; determining an output of the stochastic layer based on both the determined values of the plurality of parameters and the inputs to the stochastic layer; and providing the output of the stochastic layer to a downstream layer of the multi-layer neural network or as an output of the multi-layer neural network.

Abstract: Aspects of the subject disclosure may include, for example, obtaining video data from a single monocular camera, wherein the video data comprises a plurality of frames, wherein the camera is attached to a mobile robot that is travelling along a lane defined by a row of crops, wherein the row of crops comprises a first plant stem, and wherein the plurality of frames include a depiction of the first plant stem; obtaining robot velocity data from encoder(s), wherein the encoder(s) are attached to the robot; performing foreground extraction on each of the plurality of frames of the video data, wherein the foreground extraction results in a plurality of foreground images; and determining, based upon the plurality of foreground images and based upon the robot velocity data, an estimated width of the first plant stem. Additional embodiments are disclosed.