A review on UAV-based applications for plant disease detection and monitoring
Article
Article Title | A review on UAV-based applications for plant disease detection and monitoring |
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ERA Journal ID | 201448 |
Article Category | Article |
Authors | Kouadio, Louis, El Jarroudi, Moussa, Belabess, Zineb, Laasli, Salah-Eddine, Roni, Md Zohurul Kadir, Amine, Ibn Dahou Idrissi, Mokhtari, Nourreddine, Mokrini, Fouad, Junk, Jürgen and Lahlali, Rachid |
Journal Title | Remote Sensing |
Journal Citation | 15 (17) |
Article Number | 4273 |
Number of Pages | 23 |
Year | 2023 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2072-4292 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/rs15174273 |
Web Address (URL) | https://doi.org/10.3390/rs15174273 |
Abstract | Remote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis of current studies becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies. This study undertakes a systematic quantitative literature review to summarize existing literature and discern current research trends in UAV-based applications for plant disease detection and monitoring. Results reveal a global disparity in research on the topic, with Asian countries being the top contributing countries (43 out of 103 papers). World regions such as Oceania and Africa exhibit comparatively lesser representation. To date, research has largely focused on diseases affecting wheat, sugar beet, potato, maize, and grapevine. Multispectral, reg-green-blue, and hyperspectral sensors were most often used to detect and identify disease symptoms, with current trends pointing to approaches integrating multiple sensors and the use of machine learning and deep learning techniques. Future research should prioritize (i) development of cost-effective and user-friendly UAVs, (ii) integration with emerging agricultural technologies, (iii) improved data acquisition and processing efficiency (iv) diverse testing scenarios, and (v) ethical considerations through proper regulations. |
Keywords | unmanned aerial vehicle; plant disease; disease monitoring; image processing; machine learning |
Article Publishing Charge (APC) Funding | Other |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 300409. Crop and pasture protection (incl. pests, diseases and weeds) |
Byline Affiliations | Centre for Applied Climate Sciences |
University of Liege, Belgium | |
National Institute of Agricultural Research, Ecuador | |
National School of Agriculture, Morocco | |
University of Florida, United States | |
Luxembourg Institute of Science and Technology, Luxembourg | |
Mohammed VI Polytechnic University, Morocco |
https://research.usq.edu.au/item/z15w0/a-review-on-uav-based-applications-for-plant-disease-detection-and-monitoring
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