English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Yield assessment has been identified as critical topic for grape and wine industry. Computer vision has been applied for assessing yield, but the accuracy was greatly affected by fruit occlusion affected by leaves and other plant organs. The objective of this work was the consistent, continuous evaluation of the impact of leaf occlusions in different commercial vineyard plots at different defoliation stages. RGB (red, green and blue) images from five Tempranillo (Vitis vinifera L.) vineyards were manually acquired using a digital camera under field conditions at three different levels of defoliation: no defoliation, partial defoliation and full defoliation. Computer vision was used for the automatic detection of different canopy features, and for the calibration of regression equations for the prediction of yield computed per vine segment. Leaf occlusion rate (berry occlusion affected by leaves) was computed by machine vision in no defoliated vineyards. As occlusion rate increased, R2 between bunch pixels and yield was gradually reduced, ranging from 0.77 in low occlusion, to 0.63.

Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards