Camera apparatus and method of training and operating neural network model for enhanced foliage detection

A camera apparatus, comprising:

• control circuitry, wherein in a training phase, the control circuitry is configured to:
• generate a set of binary images of foliage masks, wherein each binary image comprises one or more foliage regions demarcated from a background non-foliage region;
• generate a modified training dataset of color images from a first set of input color images;
• and
• train a custom neural network model for foliage detection based on the generated set of binary images of foliage masks and the modified training dataset,
• wherein in the training phase, the control circuitry causes the custom neural network model to:
• learn a plurality of features related to foliage from the modified training dataset;
• further learn a color variation range of a predefined color associated with the plurality of features;
• and
• utilize a combination of the plurality of features related to foliage and the color variation range of the predefined color to obtain a trained custom neural network model, and
• wherein in an operational phase, the control circuitry is further configured to:
• capture a new color image of an agricultural field;
• remove a portion of the new color image, wherein the portion comprises pixels indicative of an artificial object in a field-of-view (FOV) of the camera apparatus;
• and
• operate the trained custom neural network model to detect one or more foliage regions in the new color image in a real time or near real time.