Grapevine Plant Image Dataset for Pruning

Abstract

Grapevine pruning is conducted during winter, and it is a very important and expensive task for wine producers managing their vineyard. During grapevine pruning every year, the past year’s canes should be removed and should provide the possibility for new canes to grow and produce grapes. It is a difficult procedure, and it is not yet fully automated. However, some attempts have been made by the research community. Based on the literature, grapevine pruning automation is approximated with the help of computer vision and image processing methods. Despite the attempts that have been made to automate grapevine pruning, the task remains hard for the abovementioned domains. The reason for this is that several challenges such as cane overlapping or complex backgrounds appear. Additionally, there is no public image dataset for this problem which makes it difficult for the research community to approach it. Motivated by the above facts, an image dataset is proposed for grapevine canes’ segmentation for a pruning task. An experimental analysis is also conducted in the proposed dataset, achieving a 67% IoU and 78% F1 score in grapevine cane semantic segmentation with the U-net model.

Plant phenotyping relevance

ブドウ樹の枝を画像から分割する公開データセットを提案し、セグメンテーション性能も評価しており、植物器官の画像計測手法・ベンチマークが中心です。

Code and data availability

The paper's own grapevine pruning image dataset (100 RGB images with hand-annotated segmentation masks) is publicly available on GitHub under CC BY 4.0, as stated in the abstract and Data Availability Statement. No author analysis code or trained model checkpoint is explicitly deposited.