Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks

Abstract

Yield estimation and forecasting are of special interest in the field of grapevine breeding and viticulture. The number of harvested berries per plant is strongly correlated with the resulting quality. Therefore, early yield forecasting can enable a focused thinning of berries to ensure a high quality end product. Traditionally yield estimation is done by extrapolating from a small sample size and by utilizing historic data. Moreover, it needs to be carried out by skilled experts with much experience in this field. Berry detection in images offers a cheap, fast and non-invasive alternative to the otherwise time-consuming and subjective on-site analysis by experts. We apply fully convolutional neural networks on images acquired with the Phenoliner, a field phenotyping platform. We count single berries in images to avoid the error-prone detection of grapevine clusters. Clusters are often overlapping and can vary a lot in the size which makes the reliable detection of them difficult. We address especially the detection of white grapes directly in the vineyard. The detection of single berries is formulated as a classification task with three classes, namely 'berry', 'edge' and 'background'. A connected component algorithm is applied to determine the number of berries in one image. We compare the automatically counted number of berries with the manually detected berries in 60 images showing Riesling plants in vertical shoot positioned trellis (VSP) and semi minimal pruned hedges (SMPH). We are able to detect berries correctly within the VSP system with an accuracy of 94.0 \% and for the SMPH system with 85.6 \%.

Plant phenotyping relevance

画像と深層学習を用いてブドウ果粒数という収量関連形質を自動抽出し、手動計数と比較検証しており、植物フェノタイピング手法が中心です。

Code and data availability

The paper describes grapevine berry image datasets (Phenoliner-acquired, annotated) and a segmentation pipeline, but no block contains any public repository, dataset deposit, or code availability statement with an authors' URL. No qualifying paper-specific public asset is present.

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