New classification of weed development stages using machine learning methods: radiomics parameters

Background: In recent years, artificial intelligence methods based on image processing have been introduced for weed identification therefore weed control. Image processing methods typically cause a heavy computer workload. Objective: The goal of this research was to create a classification model for weed development stages with better accuracy reduced workload by incorporating Region of Interest (ROI) into existing classification models. Methods: Weeds were grown and photographed in several development stages for the dataset. Using the ROI technique, commonly utilized [...]