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Counting of grapevine berries in images via semantic segmentation using convolutional neural networks
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2020-04-22 , DOI: 10.1016/j.isprsjprs.2020.04.002
Laura Zabawa , Anna Kicherer , Lasse Klingbeil , Reinhard Töpfer , Heiner Kuhlmann , Ribana Roscher

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.



中文翻译:

使用卷积神经网络通过语义分割对图像中的葡萄浆果进行计数

表型性状的提取通常非常耗时且费力。由于葡萄树的多年生特性,尤其是在葡萄栽培方面的研究仅限于现场分析。传统上熟练的专家会检查少量样本并将结果外推到整个图。因此,不同的葡萄品种和训练系统,例如垂直枝条定位(VSP)和半最小修剪树篱(SMPH)构成了不同的挑战。

在本文中,我们提出了一个基于自动图像分析的客观框架,该框架适用于两种不同的训练系统。图像由安装在改良型葡萄收获机上的相机系统半自动收集。该系统从植物的侧面产生重叠的图像。我们的框架使用卷积神经网络通过执行语义分割来检测图像中的单个浆果。然后使用连接的组件算法对每个浆果进行计数。我们将结果与Mask-RCNN(用于实例细分的最新网络)和用于计数的回归方法进行比较。本文提出的实验表明,尽管使用了不同的训练系统,我们仍能够检测图像中的绿色浆果。我们在VSP和85中达到94.0%的浆果检测精度。

更新日期:2020-04-22
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