Automated Evaluation and Comparison of Grapevine Genotypes by means of Grape Cluster Architecture
Final Report Abstract
Grape bunch architecture, which defines bunch structure and thus, its compactness, relies on a mosaic of different single traits. One major aim for (grapevine) breeding and research is an increased breeding/selection efficiency, based on an increased number of samples (high-throughput) and an improved objectivity of phenotypic data. Image-based analysis has been used often for the characterization of different bunch traits, like bunch length, width and bunch compactness. Threedimensional (3D) methods enable the acquisition of the whole bunch architecture and the distribution of berries and the diameter of all visible berries. The project “Automated Evaluation and Comparison of Grapevine Genotypes by means of Grape Cluster Architecture” aimed at the development of an automated 3D based phenotyping pipeline by using a fast, light, handheld and high-resolution 3D scanner and an intuitive, user friendly and automated software. This leads to an increased efficiency in phenotyping of bunch architecture related traits and further, much more efficient way of mapping QTLs responsible for bunch architecture traits. In this context the following contributions were achieved: An evaluation of the applicability of the method for the use in the lab. This includes a widely standardized procedure and protocol as well as a general usability for phenotypically variable bunches (loose – compact structure, color, shape and size). - An evaluation of the applicability of the method for the use directly in the field. This includes the evaluation on almost 2,000 grape bunches of phenotypic variable bunches (loose – compact structure, color, shape and size). - The derivation of phenotypes yielding results with high enough quality to support QTL mapping and genetic analyses. - A brighter understanding in the organization of QTLs for bunch architecture related traits among several populations. The concept was developed in close collaboration between IVS and JKI (and further in collaboration within different research groups at JKI), yielding an automated phenotyping system for the grape bunch architecture and the most important bunch traits, like berry number, berry diameter, berry volume or bunch length with high-throughput. The pipeline was applied for investigations of an increasing number of genotypes. In the last season of 2019, we were able to investigate grape bunch characteristics of over 4,500 grapes from the genetic repository, mapping populations and new breeding lines. The application of the workflow enables enhancement of phenotyping efficiency by a factor of ten to twelve for lab scans and further enable high-throughput and non-invasively assessments directly in the field. The phenotyping pipeline is open to all kind of users due to simple-to-handle, unharmed sensor technology, analysis software with an intuitive graphical user interface and further, minor necessity of user interaction due to automated data analysis. These are the most convenient advantages of the developed method. Furthermore, acquisition and analysis of precise sensor data takes approximately one minute and is thus much faster compared to comparable precise methods. Further, field applications can be used for repeatable screenings and comparable evaluations of large experimental plots of high varying breeding material or genetic repositories, e.g., in order to conduct comparative genetic association studies or to develop genetic markers for marker-assisted selection. This kind of phenotypic objectivity enables monitoring purposes in order to track bunch development under different environmental conditions, training systems or treatments.
Publications
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(2017): Experimental Evaluation of the Performance of Local Shape Descriptors for the Classification of 3D Data in Precision Farming. Journal of Computer and Communications, Vol. 5, No. 12
Jennifer Mack, Annatina Trakowski, Florian Rist, Katja Herzog, Reinhard Töpfer and Volker Steinhage
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High-precision 3D detection and reconstruction of grapes from laser range data for efficient phenotyping based on supervised learning (2017). Computers and Electronics in Agriculture, Vol. 135, No. 1, pp. 300- 311
Jennifer Mack, Christian Lenz, Johannes Teutrine and Volker Steinhage
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(2018): High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation. Sensors, Vol. 18, No. 3, pp. 763
Florian Rist, Katja Herzog, Jennifer Mack, Robert Richter, Volker Steinhage and Reinhard Töpfer
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(2018): Identification of co-located QTLs and genomic regions affecting grapevine cluster architecture. Theoretical and Applied Genetics 132(3)
Robert Richter, Doreen Gabriel, Florian Rist, Reinhard Töpfer and Eva Zyprian
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(2018): Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping. arXiv preprint
Bernhard Japes, Jennifer Mack, Florian Rist, Katja Herzog, Reinhard Töpfer and Volker Steinhage
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(2018): Semantic labeling and reconstruction of grape bunches from 3D range data using a new RGB-D feature descriptor. Computers and Electronics in Agriculture, Vol. 155, pp. 96-102
Jennifer Mack, Frank Schindler, Florian Rist, Katja Herzog, Reinhard Töpfer and Volker Steinhage
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(2019). Automatic Flower Number Evaluation in Grapevine Inflorescences Using RGB Images. American Journal of Enology and Viticulture 71(1)
Javier Tello, Katja Herzog, Florian Rist, Patrice This and Agnès Doligez
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(2019): Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches. Remote Sensing Vol. 11(24): 2953, December 2019
Florian Rist, Doreen Gabriel, Jennifer Mack, Volker Steinhage Reinhard Töpfer and Katja Herzog
