Open AccessA Comprehensive Survey for Weed Classification and Detection in Agriculture Lands
Author(s) -
G. Hari Krishnan,
Thiyagarajan Rajasenbagam
Publication year - 2022
Publication title -
journal of information technology and digital world
Language(s) - English
Resource type - Journals
ISSN - 2582-418X
DOI - 10.36548/jitdw.2021.4.004
Subject(s) - agriculture , scope (computer science) , weed , precision agriculture , computer science , weed control , field (mathematics) , artificial intelligence , deep learning , agricultural engineering , machine learning , engineering , geography , mathematics , agronomy , archaeology , pure mathematics , biology , programming language
In modern agriculture, there are many technologies that improve the performance of farming and production of the main plant. Few such important technologies are the machine learning and deep learning for the automatic weed classification and detection. It is very useful to control or remove the weeds in the agriculture lands by automated weed control systems. This paper gives the overall survey of the existing research of the weed classification and detection using various techniques present in the digital image processing, machine learning and deep learning field. It also covers the merits, demerits and challenges of the existing methods and the future scope of the research.