International Journal of Science, Engineering and TechnologyAuthors: Talluri Milka, Assistant Professor Mrs.M.Sivaparvathi
Abstract: In order to implement efficient weed control tactics, cotton weed categorisation is an important component of precision agriculture. Hybrid deep learning models provide a strong foundation for this classification challenge by combining several models. This method is taken to the next level with the use of active learning algorithms, which streamline the classification process by cutting down on the size of labelled datasets. Particularly for specialised jobs like weed detection in agricultural applications, the time and money spent manually labelling huge datasets may be substantial; active learning attempts to alleviate this problem. It maximises the efficiency of human annotators by picking the most informative samples to label.
