Work

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

Year: 2025

Type: article

Abstract: 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 [...]

Source: Advances in Weed Science

DOI: 10.51694/advweedsci/2025;43:00018

Authors: Eyyüp Gülbandilar, Ahmet Uludağ, Yasin Çiçek

Institutions: Eskişehir Osmangazi University, Çanakkale Onsekiz Mart Üniversitesi

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $350