HierbaNetV1: a novel feature extraction framework for deep learning-based weed identification.

Extracting the essential features and learning the appropriate patterns are the two core character traits of a convolution neural network (CNN). Leveraging the two traits, this research proposes a novel feature extraction framework code-named 'HierbaNetV1' that retrieves and learns effective features from an input image. Originality is brought by addressing the problem of varying-sized region of interest (ROI) in an image by extracting features using diversified filters.