A method to recognize and classify a bare-root plant on a conveyor surface, comprising: classifying a bare-root plant from a raw image to form a classified bare-root image based on trained parameters; and evaluating the classified bare-root plant based on trained features to assign the evaluated bare-root plant to a configured category, wherein classifying the bare-root plant comprises: receiving a real-time 2 dimensional continuous stream of raw image data from camera imaging; detecting and extracting foreground objects in the image to identify a plurality of sub-parts of the bare-root plant to form a cropped image containing only plants and minimal conveyor background; masking of disconnected components in the foreground image to remove portions of other nearby plants to form a second cropped image; calculating features for use in pixel classification based on the second cropped image; and wherein evaluating the classified bare-root comprises: classifying each pixel of the second cropped image using the trained parameters as one sub-part of the plurality of sub-parts of the bare-root plant, to identify sub-parts of the bare-root plant; calculating features of the bare-root plant based on the classified pixels for use in plant classification based on the trained features; classifying the bare-root plant into one of a plurality of categories; and dispositioning the plant based on the category. classifying a bare-root plant from a raw image to form a classified bare-root image based on trained parameters; and evaluating the classified bare-root plant based on trained features to assign the evaluated bare-root plant to a configured category, wherein classifying the bare-root plant comprises: receiving a real-time 2 dimensional continuous stream of raw image data from camera imaging; detecting and extracting foreground objects in the image to identify a plurality of sub-parts of the bare-root plant to form a cropped image containing only plants and minimal conveyor background; masking of disconnected components in the foreground image to remove portions of other nearby plants to form a second cropped image; calculating features for use in pixel classification based on the second cropped image; and receiving a real-time 2 dimensional continuous stream of raw image data from camera imaging; detecting and extracting foreground objects in the image to identify a plurality of sub-parts of the bare-root plant to form a cropped image containing only plants and minimal conveyor background; masking of disconnected components in the foreground image to remove portions of other nearby plants to form a second cropped image; calculating features for use in pixel classification based on the second cropped image; and wherein evaluating the classified bare-root comprises: classifying each pixel of the second cropped image using the trained parameters as one sub-part of the plurality of sub-parts of the bare-root plant, to identify sub-parts of the bare-root plant; calculating features of the bare-root plant based on the classified pixels for use in plant classification based on the trained features; classifying the bare-root plant into one of a plurality of categories; and classifying each pixel of the second cropped image using the trained parameters as one sub-part of the plurality of sub-parts of the bare-root plant, to identify sub-parts of the bare-root plant; calculating features of the bare-root plant based on the classified pixels for use in plant classification based on the trained features; classifying the bare-root plant into one of a plurality of categories; and dispositioning the plant based on the category. <EOS>