detection and segmentation of vine canopy in ultra-high spatial resolution RGB imagery obtained from Unmanned Aerial Vehicle (UAV) : a case study in a commercial vineyard

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dc.contributor.authorPoblete-Echeverria, Carlosen_ZA
dc.contributor.authorOlmedo, Guillermo Federicoen_ZA
dc.contributor.authorIngram, Benen_ZA
dc.contributor.authorBardeen, Matthewen_ZA
dc.date.accessioned2019-01-29T12:46:15Z
dc.date.available2019-01-29T12:46:15Z
dc.date.issued2017
dc.descriptionCITATION: Poblete-Echeverria, C., et al. 2017. detection and segmentation of vine canopy in ultra-high spatial resolution RGB imagery obtained from Unmanned Aerial Vehicle (UAV) : a case study in a commercial vineyard. Remote Sensing, 9(3):268, doi:10.3390/rs9030268.
dc.descriptionThe original publication is available at http://www.mdpi.com
dc.description.abstractThe use of Unmanned Aerial Vehicles (UAVs) in viticulture permits the capture of aerial Red-Green-Blue (RGB) images with an ultra-high spatial resolution. Recent studies have demonstrated that RGB images can be used to monitor spatial variability of vine biophysical parameters. However, for estimating these parameters, accurate and automated segmentation methods are required to extract relevant information from RGB images. Manual segmentation of aerial images is a laborious and time-consuming process. Traditional classification methods have shown satisfactory results in the segmentation of RGB images for diverse applications and surfaces, however, in the case of commercial vineyards, it is necessary to consider some particularities inherent to canopy size in the vertical trellis systems (VSP) such as shadow effect and different soil conditions in inter-rows (mixed information of soil and weeds). Therefore, the objective of this study was to compare the performance of four classification methods (K-means, Artificial Neural Networks (ANN), Random Forest (RForest) and Spectral Indices (SI)) to detect canopy in a vineyard trained on VSP. Six flights were carried out from post-flowering to harvest in a commercial vineyard cv. Carménère using a low-cost UAV equipped with a conventional RGB camera. The results show that the ANN and the simple SI method complemented with the Otsu method for thresholding presented the best performance for the detection of the vine canopy with high overall accuracy values for all study days. Spectral indices presented the best performance in the detection of Plant class (Vine canopy) with an overall accuracy of around 0.99. However, considering the performance pixel by pixel, the Spectral indices are not able to discriminate between Soil and Shadow class. The best performance in the classification of three classes (Plant, Soil, and Shadow) of vineyard RGB images, was obtained when the SI values were used as input data in trained methods (ANN and RForest), reaching overall accuracy values around 0.98 with high sensitivity values for the three classes.en_ZA
dc.description.versionPublisher's version
dc.format.extent14 pages
dc.identifier.citationPoblete-Echeverria, C., et al. 2017. detection and segmentation of vine canopy in ultra-high spatial resolution RGB imagery obtained from Unmanned Aerial Vehicle (UAV) : a case study in a commercial vineyard. Remote Sensing, 9(3):268, doi:10.3390/rs9030268
dc.identifier.issn2072-4292 (online)
dc.identifier.otherdoi:10.3390/rs9030268
dc.identifier.urihttp://hdl.handle.net/10019.1/105373
dc.identifier.urihttps://www.mdpi.com/2072-4292/9/3/268
dc.language.isoen_ZAen_ZA
dc.publisherMDPI
dc.rights.holderAuthors retain copyright
dc.subjectPrecision viticultureen_ZA
dc.subjectImage registration
dc.subjectImage analysis
dc.titledetection and segmentation of vine canopy in ultra-high spatial resolution RGB imagery obtained from Unmanned Aerial Vehicle (UAV) : a case study in a commercial vineyarden_ZA
dc.typeArticleen_ZA
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