The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses
Open accessAbstract
Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. Their use in agriculture is particularly suitable, as those areas are often vast, making ground scouting difficult, and sparsely populated, which means that injury and privacy risks are not as important as in urban settings. Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition deficiencies, and pests. This article presents a critical overview of the main advancements on the subject, focusing on the strategies that have been used to extract the information contained in the images captured during the flights. Based on the information found in more than 100 published articles and on our own research, a discussion is provided regarding the challenges that have already been overcome and the main research gaps that still remain, together with some suggestions for future research.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010) · Machine Learning · doi:10.1007/s10994-009-5152-4 · Cited by 3,515
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
Lightweight unmanned aerial vehicles will revolutionize spatial ecology
Anderson, K., & Gaston, K. J. (2013) · Frontiers in Ecology and the Environment · doi:10.1890/120150 · Cited by 1,397
Mahlein, A.-K. (2016) · Plant Disease · doi:10.1094/pdis-03-15-0340-fe · Cited by 1,363
Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. (2017) · Remote Sensing · doi:10.3390/rs9111110 · Cited by 1,318
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Bock, C. H., Poole, G. H., Parker, P. E., & Gottwald, T. R. (2010) · Critical Reviews in Plant Sciences · doi:10.1080/07352681003617285 · Cited by 964
Zarco-Tejada, P. J., González-Dugo, V., & Berni, J. A. J. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.10.007 · Cited by 915
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Cited by
Gill, T., Gill, S. K., Saini, D. K., Chopra, Y., de Koff, J. P., & Sandhu, K. S. (2022) · Phenomics · doi:10.1007/s43657-022-00048-z · Cited by 322
High-throughput phenotyping: Breaking through the bottleneck in future crop breeding
Song, P., Wang, J., Guo, X., Yang, W., & Zhao, C. (2021) · The Crop Journal · doi:10.1016/j.cj.2021.03.015 · Cited by 269
Metadata
- Authors
- Barbedo, J.
- Venue
- Drones
- Publisher
- MDPI AG
- Published
- 2019-04-20
- Volume
- 3
- Issue
- 2
- Pages
- 40
- ISSN
- 2504-446X (ISSN-L)
- OpenAlex
- W2940856955
- Cited by
- 391
- References
- 90
- FWCI
- 26.94
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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