Publicação
Yield components detection and image-based indicators for non-invasive grapevine yield prediction at different phenological phases
| dc.contributor.author | Victorino, Gonçalo | |
| dc.contributor.author | Braga, Ricardo | |
| dc.contributor.author | Santos-Victor, José | |
| dc.contributor.author | Lopes, C.M. | |
| dc.date.accessioned | 2021-01-06T10:01:49Z | |
| dc.date.available | 2021-01-06T10:01:49Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Forecasting vineyard yield with accuracy is one of the most important trends of research in viticulture today. Conventional methods for yield forecasting are manual, require a lot of labour and resources and are often destructive. Recently, image-analysis approaches have been explored to address this issue. Many of these approaches encompass cameras deployed on ground platforms that collect images in proximal range, on-the-go. As the platform moves, yield components and other image-based indicators are detected and counted to perform yield estimations. However, in most situations, when image acquisition is done in non-disturbed canopies, a high fraction of yield components is occluded. The present work’s goal is twofold. Firstly, to evaluate yield components’ visibility in natural conditions throughout the grapevine’s phenological stages. Secondly, to explore single bunch images taken in lab conditions to obtain the best visible bunch attributes to use as yield indicators. In three vineyard plots of red (Syrah) and white varieties (Arinto and Encruzado), several canopy 1 m segments were imaged using the robotic platform Vinbot. Images were collected from winter bud stage until harvest and yield components were counted in the images as well as in the field. At pea-sized berries, veraison and full maturation stages, a bunch sample was collected and brought to lab conditions for detailed assessments at a bunch scale. At early stages, all varieties showed good visibility of spurs and shoots, however, the number of shoots was only highly and significantly correlated with the yield for the variety Syrah. Inflorescence and bunch occlusion reached high percentages, above 50 %. In lab conditions, among the several bunch attributes studied, bunch volume and bunch projected area showed the highest correlation coefficients with yield. In field conditions, using non-defoliated vines, the bunch projected area of visible bunches presented high and significant correlation coefficients with yield, regardless of the fruit’s occlusion. Our results show that counting yield components with image analysis in non-defoliated vines may be insufficient for accurate yield estimation. On the other hand, using bunch projected area as a predictor can be the best option to achieve that goal, even with high levels of occlusion | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | OENO One 2020, 54, 4, 833-848 | pt_PT |
| dc.identifier.doi | 10.20870/oeno-one.2020.54.4.3616 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.5/20753 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | IVES - International Viticulture and Enology Society | pt_PT |
| dc.subject | precision viticulture | pt_PT |
| dc.subject | yield estimation | pt_PT |
| dc.subject | image analysis | pt_PT |
| dc.subject | yield components occlusion | pt_PT |
| dc.title | Yield components detection and image-based indicators for non-invasive grapevine yield prediction at different phenological phases | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | OENO One | pt_PT |
| person.familyName | Braga | |
| person.familyName | Antunes Lopes | |
| person.givenName | Ricardo | |
| person.givenName | Carlos Manuel | |
| person.identifier.ciencia-id | 3A1E-764B-F58C | |
| person.identifier.orcid | 0000-0001-9528-2722 | |
| person.identifier.orcid | 0000-0003-2456-1200 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 50ebf808-c78a-4729-9339-3f8dfc09eac5 | |
| relation.isAuthorOfPublication | ddf650c0-4e65-4c8e-aa66-4836227ccf9d | |
| relation.isAuthorOfPublication.latestForDiscovery | ddf650c0-4e65-4c8e-aa66-4836227ccf9d |
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