You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

A systematic review of UAV and AI integration for targeted disease detection, weed management, and pest control in precision agriculture

Authors:

Iftekhar Anam , Naiem Arafat , Md Sadman Hafiz , Jamin Rahman Jim , Md Mohsin Kabir, M.F. Mridha

Publication Type:

Journal article

Venue:

Smart Agricultural Technology

Publisher:

Elsevier

DOI:

https://doi.org/10.1016/j.atech.2024.100647


Abstract

Unmanned aerial vehicles (UAV), often unmanned aerial systems, are increasingly used in many industries, such as agriculture, forestry, the military, and disaster management. This is because they have the potential to perform tasks remotely without human intervention. This study comprehensively analyzes the latest developments in UAV technology for crop disease detection, weed management, and pest control. The focus of this study is on the incorporation of machine learning and deep learning algorithms into these UAV systems. We have conducted a thorough analysis of recent studies, particularly 2022–24, to evaluate the effectiveness of different unmanned aerial vehicle models, sensor types, and computational methods to improve crop monitoring and disease control strategies. This study highlights the remarkable agricultural production and sustainability improvements that UAVs enable. These vehicles provide accurate and practical information on crop health and the presence of weeds, detecting diseases and controlling pests, leading to valuable insights. However, obstacles remain in terms of data management, algorithmic complexity, and operational constraints under different environmental conditions. We discuss potential solutions and areas for future research to address current shortcomings and stimulate further improvements in agricultural operations using unmanned aerial vehicles. This in-depth exploration highlights the significant opportunities that unmanned aerial vehicles offer in agriculture and draws attention to critical areas where innovation and research are still needed.

Bibtex

@article{Anam7055,
author = {Iftekhar Anam and Naiem Arafat and Md Sadman Hafiz and Jamin Rahman Jim and Md Mohsin Kabir and M.F. Mridha},
title = {A systematic review of UAV and AI integration for targeted disease detection, weed management, and pest control in precision agriculture},
editor = {Elsevier},
volume = {9},
pages = {1--24},
month = {November},
year = {2024},
journal = {Smart Agricultural Technology},
publisher = {Elsevier},
url = {http://www.es.mdu.se/publications/7055-}
}