Review of Segmentation Techniques for Weed Detection in Agricultural Crops

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.