Dr.-Ing.

Gruna, Robin

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  • Publication
    Developing a handheld NIR sensor for the detection of ripening in grapevine
    ( 2021)
    Gebauer, Lucie
    ;
    Krause, Julius
    ;
    Zheng, Xiaorong
    ;
    Kronenwett, Felix
    ;
    Gruna, Robin
    ;
    Töpfer, Reinhard
    ;
    Kicherer, Anna
    It has already been proven that near infrared (NIR) reflectance spectroscopy can be used to measure the ripeness of grapes by the determination of reducing sugar and acid contents. Until now, winegrowers need to collect a random one hundred berries sample per plot, to measure these parameters destructively for the estimation of the ideal harvest time of the gained product. Meanwhile, inexpensive sensors are available, to build convenient instruments for the non-destructive, low-priced and fast control of ripening parameters in the vineyard. For this, a small device including a NIR sensor (900 nm - 1700 nm / 1300 nm - 2600 nm) was built from a Raspberry Pi 3 and a NIR sensor. Spectra of individual berries, sampled from six different Vitis vinifera (L.) cultivars (Riesling, Chardonnay, Pinot Noir, Dornfelder, Pinot Gris and Dakapo) were collected. Corresponding reference data were determined with high performance liquid chromatography (HPLC). Samples were taken from different fruit-, as well as cluster zones and from the beginning of veraison until after harvest, to ensure a broad range of ingredients and the ripening properties of different berries from the vine. White, as well as red varieties were used to establish the built sensor as a viable tool for ripening prediction for mainly cultivated vines. Spectra of teinturier berries with strongly coloured flesh or skin were collected to verify its accuracy for these cultivars, too. This study is the first that systematically investigates the ripening parameters of a whole vineyard with a handheld sensor. The sensor can be used in viticulture practice to detect the ripening process and ideal harvest time due to effectiveness and simplicity.
  • Publication
    Phenoliner 2.0: RGB and near-infrared (NIR) image acquisition for an efficient phenotyping in grapevine research
    ( 2021)
    Zheng, Xiaorong
    ;
    Krause, Julius
    ;
    Fischer, Benedikt orcid-logo
    ;
    Gruna, Robin
    ;
    Töpfer, Reinhard
    ;
    Kicherer, Anna
    In grapevine research, phenotyping needs to be done for different traits such as abiotic and biotic stress. This phenotypic data acquisition is very time-consuming and subjective due to the limitation of manual visual estimation. Sensor-based approaches showed an improvement in objectivity and throughput in the past. For example, the 'Phenoliner' a phenotyping platform, based on a modified grape harvester, is equipped with two different sensor systems to acquire images in the field. It has so far been used in grapevine research for different research questions to test and apply different sensor systems. However, the driving speed for data acquisition has been limited to 0.5 - 1 km/ h due to capacity of image acquisition frequency and storage. Therefore, a faster automatic data acquisition with high objectivity and precision is desirable to increase the phenotyping efficiency. To this aim, in the present study a prism-based simultaneous multispectral camera system was installed in the tunnel of the 'Phenoliner' with an artificial broadband light source for image acquisition. It consists of a visible color channel from 400 to 670 nm, a near infrared (NIR) channel from 700 to 800 nm, and a second NIR channel from 820 to 1,000 nm. Compared to the existing camera setup, image recording could be improved to at least 10 images per second and a driving speed of at least 5 km/h. Each image is geo-referenced using a real-time-kinematic (RTK)-GPS system. The setup of the sensor system was tested on seven cultivars (Riesling, Pinot Noir, Chardonnay, Dornfelder, Dapako, Pinot Gris, and Phoenix) with and without symptoms of biotic stress in the vineyards of Geilweilerhof, Germany. Image analysis aims to segment images into four categories: background, leaves, grapes and wood to further detect the biotic stress status in these categories. Therefore, images have been annotated accordingly and first results will be shown.