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

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

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 …

Automated Phenotyping of Epicuticular Waxes of Grapevine Berries Using Light Separation and Convolutional Neural Networks

In viticulture the epicuticular wax as the outer layer of the berry skin is known as trait which is correlated to resilience towards Botrytis bunch rot. Traditionally this trait is classified using the OIV descriptor 227 (berry bloom) in a time consuming way resulting in subjective and error-prone phenotypic data. In the present study an objective, fast and sensor-based approach was developed to monitor berry bloom. From the technical point-of-view, it is known that the measurement of different illumination components conveys important information about observed object surfaces. A Mobile Light…

Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks

The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-destructively, and its process can be automated. In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest. We use generative adversarial networks, a deep learning-based approach that generates a likely s…

An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networks

In (grapevine) breeding programs and research, periodic phenotyping and multi-year monitoring of different grapevine traits, like growth or yield, is needed especially in the field. This demand imply objective, precise and automated methods using sensors and adaptive software. This work presents a proof-of-concept analyzing RGB images of different growth stages of grapevines with the aim to detect and quantify promising plant organs which are related to yield. The input images are segmented by a Fully Convolutional Neural Network (FCN) into object and background pixels. The objects are plant o…

Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields

The berry size is one of the most important fruit traits in grapevine breeding. Non-invasive, image-based phenotyping promises a fast and precise method for the monitoring of the grapevine berry size. In the present study an automated image analyzing framework was developed in order to estimate the size of grapevine berries from images in a high-throughput manner. The framework includes (i) the detection of circular structures which are potentially berries and (ii) the classification of these into the class 'berry' or 'non-berry' by utilizing a conditional random field. The approach used the c…

Segment Anything 用于葡萄串结构和浆果属性的综合分析

Segment Anything for comprehensive analysis of grapevine cluster architecture and berry properties

本研究利用 Segment Anything Model (SAM) 自动分割葡萄串图像中的浆果，实现高精度识别（Pearson r2=0.96），并通过线性回归校正可见性偏差（调整后 R2=0.87），进而计算葡萄串结构和紧实度特征，为葡萄园图像处理提供可扩展方案。