The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Enhancing Crop Yield and Reducing Herbicide Reliance Through Computer Vision Techniques
|
|
Author(s): Rashmi Agrawal (Manav Rachna International Institute of Research and Studies, India)and Parul Gandhi (Manav Rachna International Institute of Research and Studies, India)
Copyright: 2027
Pages: 17
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/407604
Purchase
|
Abstract
Weed detection plays an important role in effective weed management strategies, contributing to enhanced crop yield and reduced reliance on herbicides. Traditional methods of weed detection mostly suffer from limitations in accuracy, efficiency, and scalability. Manual inspection and monitoring are labour-intensive and time-consuming, deterring their practicality for large-scale farming operations. In recent years, the emergence of computer vision techniques has changed the field of weed detection. Leveraging advances in machine learning and image processing, computer vision offers automated and efficient solutions for weed detection. Among various available techniques, detection using bounding boxes proved significant due to its effectiveness in localizing and distinguishing weeds within crop images. This article explores the role of computer vision in weed detection, specifically focusing on bounding boxes. The article highlights the significance of computer vision techniques, particularly detection using bounding boxes, in automating weed detection processes.
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|