Review of Segmentation Techniques for Weed Detection in Agricultural Crops
Year: 2025
Type: conference-paper
Abstract: This study explores the role of deep learning in identifying and managing weeds in agriculture, a critical challenge for enhancing crop productivity. As the global population grows, increasing food production is essential. Weeds significantly hinder crop growth, making accurate identification vital. Deep learning techniques, which analyze elements like color, form, texture, and spectrum, offer promising solutions for distinguishing between crops and weeds. This review examines various segmentation techniques used in weed identification, comparing their effectiveness and potential for practical application. The findings aim to advance weed management strategies, contributing to improved agricultural productivity and the development of automated systems for precise weed detection.
Source: Procedia Computer Science
Authors: Akanksha Bodhale, Seema Verma
Institution: Banasthali University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4410301149