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Yield components detection and image-based indicators for non-invasive grapevine yield prediction at different phenological phases
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Orientador(es)
Resumo(s)
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
Descrição
Palavras-chave
precision viticulture yield estimation image analysis yield components occlusion
Contexto Educativo
Citação
OENO One 2020, 54, 4, 833-848
Editora
IVES - International Viticulture and Enology Society