Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery. Pádua, L., Marques, P., Hruška, J., Adão, T., Peres, E., Morais, R., & Sousa, J., J. Remote Sensing 2018, Vol. 10, Page 1907, 10(12):1907, Multidisciplinary Digital Publishing Institute, 11, 2018. ![Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
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Website doi abstract bibtex This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.
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title = {Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery},
type = {article},
year = {2018},
keywords = {padua2018multitemporalvineyard},
pages = {1907},
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abstract = {This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.},
bibtype = {article},
author = {Pádua, Luís and Marques, Pedro and Hruška, Jonáš and Adão, Telmo and Peres, Emanuel and Morais, Raul and Sousa, Joaquim J.},
doi = {10.3390/RS10121907},
journal = {Remote Sensing 2018, Vol. 10, Page 1907},
number = {12}
}
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