The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges
Open accessAbstract
Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India’s total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers’ reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS).
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Cites
Unmanned aerial systems for photogrammetry and remote sensing: A review
Colomina, I., & Molina, P. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.02.013 · Cited by 2,954
The application of small unmanned aerial systems for precision agriculture: a review
Zhang, C., & Kovacs, J. M. (2012) · Precision Agriculture · doi:10.1007/s11119-012-9274-5 · Cited by 2,012
A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming
Farooq, M. S., Riaz, S., Abid, A., Abid, K., & Naeem, M. A. (2019) · IEEE Access · doi:10.1109/access.2019.2949703 · Cited by 896
Review on Application of Drone Systems in Precision Agriculture
Mogili, U. R., & Deepak, B. B. V. L. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.07.063 · Cited by 869
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Detection and classification of soybean pests using deep learning with UAV images
Tetila, E. C., Machado, B. B., Astolfi, G., Belete, N. A. D. S., Amorim, W. P., Roel, A. R., & Pistori, H. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105836 · Cited by 283
Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning
Nguyen, C., Sagan, V., Maimaitiyiming, M., Maimaitijiang, M., Bhadra, S., & Kwasniewski, M. T. (2021) · Sensors · doi:10.3390/s21030742 · Cited by 281
Reflectance assessment of mite effects on apple trees
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Metadata
- Authors
- Velusamy, P., Rajendran, S., Mahendran, R. K., Naseer, S., Shafiq, M., & Choi, J.-G.
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2021-12-29
- Volume
- 15
- Issue
- 1
- Pages
- 217
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W4200421609
- Cited by
- 303
- References
- 39
- FWCI
- 19.42
- Type
- journal article
- Language
- en
- Open access
- gold
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