Abstract
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. For every input sample, 3,872 feature maps are generated with multiple levels of complexity. The proposed method integrates low-level and high-level features thus allowing the model to learn intensive and diversified features. As a follow-up of this research, a crop-weed research dataset termed 'SorghumWeedDataset_Classification' is acquired and created. This dataset is tested on HierbaNetV1 which is compared against pre-trained models and state-of-the-art (SOTA) architectures. Experimental results show HierbaNetV1 outperforms other architectures with an accuracy of 98.06%. An ablation study and component analysis are conducted to demonstrate the effectiveness of HierbaNetV1. Validated against benchmark weed datasets, the study also exhibits that our suggested approach performs well in terms of generalization across a wide variety of crops and weeds. To facilitate further research, HierbaNetV1 weights and implementation are made accessible to the research community on GitHub. To extend the research to practicality, the proposed method is incorporated with a real-time application named HierbaApp that assists farmers in differentiating crops from weeds. Future enhancements for this research are outlined in this article and are currently underway.