High-throughput phenotyping of yield parameters for modern grapevine breeding

Weinbau wird auf 1% der deutschen Agrarfläche betrieben. Auf dieser vergleichsweise kleinen Anbaufläche wird jedoch ein Drittel aller in der deutschen Landwirtschaft verwendeten Fungizide appliziert, was auf die Einführung von Schaderregern im 19. Jahrhundert zurück zu führen ist. Für einen nachhaltigen Anbau ist eine Reduktion des Pflanzenschutzmittelaufwands dringend notwendig. Dieses Ziel kann durch die Züchtung und den Anbau neuer, pilzwiderstandsfähiger Rebsorten erreicht werden. Die Rebenzüchtung als solche ist sehr zeitaufwendig, da die Entwicklung neuer Rebsorten 20 bis 25 Jahre dauert. Der Einsatz der markergestützten Selektion (MAS) erhöht die Effizienz der Selektion in der Rebenzüchtung fortwährend. Eine weitere Effizienzsteigerung ist mit der andauernden Verbesserung der Hochdurchsatz Genotypisierung zu erwarten. Im Vergleich zu den Methoden der Genotypisierung ist die Qualität, Objektivität und Präzision der traditionellen Phänotypisierungsmethoden begrenzt. Die Effizienz in der Rebenzüchtung soll mit der Entwicklung von Hochdurchsatz Methoden zur Phänotypisierung durch sensorgestützte Selektion weiter gesteigert werden. Hierfür sind bisher vielfältige Sensortechniken auf dem Markt verfügbar. Das Spektrum erstreckt sich von RGB-Kameras über Multispektral-, Hyperspektral-, Wärmebild- und Fluoreszenz- Kameras bis hin zu 3D-Techniken und Laserscananwendungen. Die Phänotypisierung von Pflanzen kann unter kontrollierten Bedingungen in Klimakammern oder Gewächshäusern beziehungsweise im Freiland stattfinden. Die Möglichkeit einer standardisierten Datenaufnahme nimmt jedoch kontinuierlich ab. Bei der Rebe als Dauerkultur erfolgt die Aufnahme äußerer Merkmale, mit Ausnahme junger Sämlinge, deshalb auch überwiegend im Freiland. Variierende Lichtverhältnisse, Ähnlichkeit von Vorder- und Hintergrund sowie Verdeckung des Merkmals stellen aus methodischer Sicht die wichtigsten Herausforderungen in der sensorgestützen Merkmalserfassung dar. Bis heute erfolgt die Aufnahme phänotypischer Merkmale im Feld durch visuelle Abschätzung. Hierbei werden die BBCH Skala oder die OIV Deskriptoren verwendet. Limitierende Faktoren dieser Methoden sind Zeit, Kosten und die Subjektivität bei der Datenerhebung. Innerhalb des Züchtungsprogramms kann daher nur ein reduziertes Set an Genotypen für ausgewählte Merkmale evaluiert werden. Die Automatisierung, Präzisierung und Objektivierung phänotypischer Daten soll dazu führen, dass (1) der bestehende Engpass an phänotypischen Methoden verringert, (2) die Effizienz der Rebenzüchtung gesteigert, und (3) die Grundlage zukünftiger genetischer Studien verbessert wird, sowie (4) eine Optimierung des weinbaulichen Managements stattfindet. Stabile und über die Jahre gleichbleibende Erträge sind für eine Produktion qualitativ hochwertiger Weine notwendig und spielen daher eine Schlüsselrolle in der Rebenzüchtung. Der Fokus dieser Studie liegt daher auf Ertragsmerkmalen wie der Beerengröße, Anzahl der Beeren pro Traube und Menge der Trauben pro Weinstock. Die verwandten Merkmale Traubenarchitektur und das Verhältnis von generativem und vegetativem Wachstum wurden zusätzlich bearbeitet. Die Beurteilung von Ertragsmerkmalen auf Einzelstockniveau ist aufgrund der genotypischen Varianz und der Vielfältigkeit des betrachteten Merkmals komplex und zeitintensiv. Als erster Schritt in Richtung Hochdurchsatz (HT) Phänotypisierung von Ertragsmerkmalen wurden zwei voll automatische Bildinterpretationsverfahren für die Anwendung im Labor entwickelt. Das Cluster Analysis Tool (CAT) ermöglicht die bildgestützte Erfassung der Traubenlänge, -breite und -kompaktheit, sowie der Beerengröße. Informationen über Anzahl, Größe (Länge, Breite) und das Volumen der einzelnen Beeren liefert das Berry Analysis Tool (BAT). Beide Programme ermöglichen eine gleichzeitige Erhebung mehrerer, präziser phänotypischer Merkmale und sind dabei schnell, benutzerfreundlich und kostengünstig. Die Möglichkeit, den Vorder- und Hintergrund in einem Freilandbild zu unterscheiden, ist besonders in einem frühen Entwicklungsstadium der Rebe aufgrund der fehlenden Laubwand schwierig. Eine Möglichkeit, die beiden Ebenen in der Bildanalyse zu trennen, ist daher unerlässlich. Es wurde eine berührungsfreie, schnelle sowie objektive Methode zur Bestimmung des Winterschnittholzgewichts, welches das vegetative Wachstum der Rebe beschreibt, entwickelt. In einem innovativen Ansatz wurde unter Kombination von Tiefenkarten und Bildsegmentierung die sichtbare Winterholzfläche im Bild bestimmt. Im Zuge dieser Arbeit wurde die erste HT Phänotypisierungspipeline für die Rebenzüchtung aufgebaut. Sie umfasst die automatisierte Bildaufnahme im Freiland unter Einsatz des PHENObots, das Datenmanagement mit Datenanalyse sowie die Interpretation des erhaltenen phänotypischen Datensatzes. Die Basis des PHENObots ist ein automatisiert gesteuertes Raupenfahrzeug. Des Weiteren umfasst er ein Multi-Kamera- System, ein RTK-GPS-System und einen Computer zur Datenspeicherung. Eine eigens entwickelte Software verbindet die Bilddaten mit der Standortreferenz. Diese Referenz wird anschließend für das Datenmanagement in einer Datenbank verwendet. Um die Funktionalität der Phänotypisierungspipeline zu demonstrieren, wurden die Merkmale Beerengröße und -farbe im Rebsortiment des Geilweilerhofes unter Verwendung des Berries In Vineyard (BIVcolor) Programms erfasst. Im Durschnitt werden 20 Sekunden pro Weinstock für die Bildaufnahme im Feld benötigt, gefolgt von der Extraktion der Merkmale mittels automatischer, objektiver und präziser Bildauswertung. Im Zuge dieses Versuches konnten mit dem PHENObot 2700 Weinstöcke in 12 Stunden erfasst werden, gefolgt von einer automatischen Bestimmung der Merkmale Beerengröße und -farbe aus den Bildern. Damit konnte die grundsätzliche Machbarkeit bewiesen werden. Diese Pilotpipeline bietet nun die Möglichkeit zur Entwicklung weiterer innovativer Programme zur Erhebung neuer Merkmale sowie die Integration zusätzlicher Sensoren auf dem PHENObot.Grapevine is grown on about 1% of the German agricultural area requiring one third of all fungicides sprayed due to pathogens being introduced within the 19th century. In spite of this requirement for viticulture a reduction is necessary to improve sustainability. This objective can be achieved by growing fungus resistant grapevine cultivars. The development of new cultivars, however, is very time-consuming, taking 20 to 25 years. In recent years the breeding process could be increased considerably by using marker assisted selection (MAS). Further improvements of MAS applications in grapevine breeding will come along with developing of faster and more cost efficient high-throughput (HT) genotyping methods.Complementary to genotyping techniques the quality, objectivity and precision of current phenotyping methods is limited and HT phenotyping methods need to be developed to further increase the efficiency of grapevine breeding through sensor assisted selection. Many different types of sensors technologies are available ranging from visible light sensors (Red Green Blue (RGB) cameras), multispectral, hyperspectral, thermal, and fluorescence cameras to three dimensional (3D) camera and laser scan approaches. Phenotyping can either be done under controlled environments (growth chamber, greenhouse) or can take place in the field, with a decreasing level of standardization. Except for young seedlings, grapevine as a perennial plant needs ultimately to be screened in the field. From a methodological point of view a variety of challenges need to be considered like the variable light conditions, the similarity of fore- and background, and in the canopy hidden traits.The assessment of phenotypic data in grapevine breeding is traditionally done directly in the field by visual estimations. In general the BBCH scale is used to acquire and classify the stages of annual plant development or OIV descriptors are applied to assess the phenotypes into classes. Phenotyping is strongly limited by time, costs and the subjectivity of records. Therefore, only a comparably small set of genotypes is evaluated for certain traits within the breeding process. Due to that limitation, automation, precision and objectivity of phenotypic data evaluation is crucial in order to (1) reduce the existing phenotyping bottleneck, (2) increase the efficiency of grapevine breeding, (3) assist further genetic studies and (4) ensure improved vineyard management. In this theses emphasis was put on the following aspects: Balanced and stable yields are important to ensure a high quality wine production playing a key role in grapevine breeding. Therefore, the main focus of this study is on phenotyping different parameters of yield such as berry size, number of berries per cluster, and number of clusters per vine. Additionally, related traits like cluster architecture and vine balance (relation between vegetative and generative growth) were considered. Quantifying yield parameters on a single vine level is challenging. Complex shapes and slight variations between genotypes make it difficult and very time-consuming.As a first step towards HT phenotyping of yield parameters two fully automatic image interpretation tools have been developed for an application under controlled laboratory conditions to assess individual yield parameters. Using the Cluster Analysis Tool (CAT) four important phenotypic traits can be detected in one image: Cluster length, cluster width, berry size and cluster compactness. The utilization of the Berry Analysis Tool (BAT) provides information on number, size (length and width), and volume of grapevine berries. Both tools offer a fast, user-friendly and cheap procedure to provide several precise phenotypic features of berries and clusters at once with dimensional units in a shorter period of time compared to manual measurements.The similarity of fore- and background in an image captured under field conditions is especially difficult and crucial for image analysis at an early grapevine developmental stage due to the missing canopy. To detect the dormant pruning wood weight, partly determining vine balance, a fast and non-invasive tool for objective data acquisition in the field was developed. In an innovative approach it combines depth map calculation and image segmentation to subtract the background of the vine obtaining the pruning area visible in the image. For the implementation of HT field phenotyping in grapevine breeding a phenotyping pipeline has been set up. It ranges from the automated image acquisition directly in the field using the PHENObot, to data management, data analysis and the interpretation of obtained phenotypic data for grapevine breeding aims. The PHENObot consists of an automated guided tracked vehicle system, a calibrated multi camera system, a Real-Time-Kinematic GPS system and a computer for image data handling. Particularly developed software was applied in order to acquire geo referenced images directly in the vineyard. The geo-reference is afterwards used for the post-processing data management in a database. As phenotypic traits to be analysed within the phenotyping pipeline the detection of berries and the determination of the berry size and colour were considered. The highthroughput phenotyping pipeline was tested in the grapevine repository at Geilweilerhof to extract the characteristics of berry size and berry colour using the Berries In Vineyards (BIVcolor) tool. Image data acquisition took about 20 seconds per vine, which afterwards was followed by the automatic image analysis to extract objective and precise phenotypic data. In was possible to capture images of 2700 vines within 12 hours using the PHENObot and subsequently automatic analysis of the images and extracting berry size and berry colour. With this analysis proof of principle was demonstrated. The pilot pipeline providesthe basis for further development of additional evaluation modules as well as the integration of other sensors

An expert system for automatically pruning vines

Vine pruning is an important part of vineyard management, and pruning is the most expensive task in the vineyard which has not yet been automated. Every year, most new canes must be removed from the vine, and the choice of canes to retain impacts vine yield. To automate the process of vine pruning, a vine pruning robot must make decisions on what canes to remove or to keep, based on a 3D topological model of the structure of the vine. In this paper we present an Artificial Intelligence (AI) system for making these decisions, developed and evaluated using simulated vines. A viticulture expert evaluated our approach by comparing it to pruning decisions made by a pruner with a skill level typical of human pruners. Our system successfully pruned 30% of vines better than the human and 89% at least as well. These results demonstrate that the vine pruning problem is solvable using current computing technologies, and that automating the pruning process has the potential to improve vine quality and yield

Manipulador robótico para poda automática (Projecto ROMOVI)

Um dos grandes desafios atuais da robótica para agricultura é o desenvolvimento de robôs capazes de executar a poda automática de forma eficiente. Neste projecto pretende-se estudar um manipulador e respectiva ferramenta de corte para instalação no AGROB V16 e que possa ser utilizado em contextos de aplicação na poda automática de vinhas. Será necessário implementar um ambiente simulado do sistema constituído pelo robô AGROB V16 que permita estudar as diferentes abordagens para o planeamento de trajectórias do manipulador. Os testes dos diferentes planeadores estado da arte (implementados em contexto de código aberto) será realizado em ambiente ROS e dividido em duas componentes: simulação e ambiente laboratorial. A componente de percepção visual da vinha não se insere no contexto desta proposta de trabalho.One of the biggest challenges of the agricultural robotics is the development of robots that are able to execute automatic pruning in a efficient way. This master thesis aims to study specific manipulator and its pruning tool that will be installed in AGROB V16 robot for application in the context of automatic pruning tasks for vineyard context. It will be necessary to implement a simulated environment, that has AGROB V16 robot and vineyard, in order to study the different approaches for trajectory planning. The tests of the different state of art planners (open source) will be done in the ROS platform and it is divided in two main components: simulation and laboratory environment. The visual perception component of the vine won't be taken in account in the context of this work proposal

Evaluating cane pruning decision criteria and the identification of grapevine pruning styles

Winter pruning is the highest yearly expenditure in the typical New Zealand vineyard budget, yet few attempts have been made to bring quantitative measurement tools into its management. The research presented here constitutes first steps towards this end, in tandem with University of Canterbury researchers working towards an artificially intelligent pruning robot. In pursuit of information regarding cane pruning preferences and decision-making criteria, a two-part survey was conducted in the regions of Marlborough, Hawke’s Bay, Waipara and Central Otago. Part One of this survey asked participants to rate a set of already-made cane pruning decisions for one (cv.) Sauvignon Blanc vine. Participants rated these decisions on 24 individual pruning criteria and also provided two overall assessments. One of these overall assessments was recorded before participants rated the decisions on the 24 individual criteria, and a second overall assessment was recorded after such time. All ratings were collected via Qualtrics software, either online or via the Qualtrics offline survey application. Part Two of the survey asked participants to indicate, with highlighter pens on paper, their own preferred pruning decisions for the same vine. Linear Models, based on the relationship between the individual criteria and overall assessments (Part One), have revealed spur and cane position to be the dominant influencing factors in the pruning of the subject vine. Participant first impressions, as measured by the first overall assessment (before the individual criteria ratings), were almost exclusively reflective of participant attitude towards spur and cane position. The dominance of position was corroborated by Correspondence Analysis of the preferred pruning decisions (Part Two), which suggested that the decision to modify the vine’s cane or spur position was a fundamental point of divide within participant responses. In a related finding, results from Principal Component Analysis (Part One) have suggested that overall impressions were a heavy influence throughout the course of participant responses to Part One of this study. By extension, this finding suggested that attitudes towards position, which were strongly linked to participant overall assessments, permeated into participant attitudes towards other aspects of the presented decision set (Part One). Generally speaking, the dominance of a single group of decision-making criteria calls for further investigation into how pruning is conceptualised as a task. Results from this study suggest that there exists a somewhat broad, non-specific, view of whether or not a particular set of spur and cane selections are acceptable. This finding, while perhaps not immediately impactful for practitioners, has considerable implication for the design of future pruning research, as well as for the evaluation of artificially intelligent pruning. This research also reports the detection of pruning preference (Part One and Part Two) groups, based on region and organisational role. Correspondence Analysis and Multiple Correspondence Analysis (Part Two) have revealed that participants from Hawke’s Bay and, particularly, Central Otago tended towards a decision to restructure the subject vine by not leaving a spur from its existing right half. This was in contrast to those participants from Marlborough and Waipara who tended towards a maintaining of the current vine configuration. Aside from these differing propensities to restructure the vine, several regions were associated with unique spur and cane selections. It is unclear at present whether regional differences are due to social influences, regional viticulture conditions, cultivar familiarity, or some unknown factor. Participants also differed in their preferences when grouped based upon their organisational role. Those participants identifying exclusively as labourers were considerably less likely to restructure the vine, compared to those participants identifying as supervisors, managers, or proprietors. Managerial implications of this finding are discussed, with several potential remedies explored