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-Echeverría, Carlos
dc.contributor.authorOlmedo, Guillermo Federico
dc.contributor.authorIngram, Benjamin R.
dc.contributor.authorBardeen, Matthew
dc.date.accessioned2017-09-05T14:12:58Z
dc.date.available2017-09-05T14:12:58Z
dc.date.issued2017-03-15
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_UK
dc.identifier.citationPoblete-Echeverría C, Olmedo GF, Ingram B, Bardeen M. (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, Volume 9, Issue 5, March 2017, Article number 268en_UK
dc.identifier.issn2072-4292
dc.identifier.urihttp://dx.doi.org/10.3390/rs9030268
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/12444
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International (CC BY 4.0) You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectprecision viticultureen_UK
dc.subjectremote sensingen_UK
dc.subjectspatial variabilityen_UK
dc.subjectimage analysisen_UK
dc.subjectrandom foresten_UK
dc.subjectartificial neural networken_UK
dc.subjectclassificationen_UK
dc.subjectOtsu methoden_UK
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_UK
dc.typeArticleen_UK

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