Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks

Abstract

The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges. In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH.

Plant phenotyping relevance

ブドウ果粒数という植物形質を、画像収集・セマンティックセグメンテーション・連結成分解析で自動抽出する方法が研究の中心であり、比較評価も実施している。

Code and data availability

The paper's grapevine berry image dataset, annotations, and authors' analysis code are not stated as publicly available in the supplied blocks. The two allowed URLs are third-party resources: the NeuroSYS objects_counting_dmap repository is an external implementation used for the U-Net density-map baseline, and matterp

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