Development and assessment of a multi-sensor platform for precision phenotyping of small grain cereals under field conditions

The growing world population, changing food habits especially to increased meat consumption in newly industrialized countries, the growing demand for energy and the climate change pose major challenges for tomorrows agriculture. The agricultural output has to be increased by 70% by 2050 to achieve food and energy security for the future and 90% of this increase must be achieved by increasing yields on existing agricultural land. Achieving this increase in yield is one of the biggest challenges for the global agriculture and requires, among other things, an efficient breeding of new, higher-yielding varieties adapted to the predicted climate change. To achieve this goal, new methods need to be established in plant breeding which include efficient genotyping and phenotyping approaches of crops. Enormous progress has been achieved in the field of genotyping which enables to gain a better understanding of the molecular basis of complex traits. However, phenotyping must be considered as equally important as genomic approaches rely on high quality phenotypic data and as efficient phenotyping enables the identification of superior lines in breeding programs. In contrast to the rapid development of genotyping approaches, phenotyping methods in plant breeding have changed only little in recent decades which is also referred to as phenotyping bottleneck. Due to this discrepancy between available phenotypic and genotypic information a significant potential for crop improvement remains unexploited. The aim of this work was the development and evaluation of a precision phenotyping platform for the non-invasive measurement of crops under field conditions. The developed platform is assembled of a tractor with 80 cm ground clearance, a carrier trailer and a sensor module attached to the carrier trailer. The innovative sensors for plant phenotyping, consisting of several 3D Time-of-Flight cameras, laser distance sensors, light curtains and a spectral imaging camera in the near infrared reflectance (NIR) range, and the entire system technology for data acquisition were fully integrated into the sensor module. To operate the system, software with a graphical user interface has been developed that enables recording of sensor raw data with time- and location information which is the basis of a subsequent sensor and data fusion for trait determination. Data analysis software with a graphical user interface was developed under Matlab. This software applies all created sensor models and algorithms on sensor raw data for parameter extraction, enables the flexible integration of new algorithms into the data analysis pipeline, offers the opportunity to generate and calibrate new sensor fusion models and allows for trait determination. The developed platform facilitates the simultaneous measurement of several plant parameters with a throughput of over 2,000 plots per day. Based on data of the years 2011 and 2012, extensive calibrations were developed for the traits plant height, dry matter content and biomass yield employing triticale as a model species. For this purpose, 600 plots were grown each year and recorded twice with the platform followed by subsequent phenotyping with state-of-the-art methods for reference value generation. The experiments of each year were subdivided into three measurements at different time points to incorporate information of three different developmental stages of the plants into the calibrations. To validate the raw data quality and robustness of the data collection and reduction process, the technical repeatability for all developed data analysis algorithms was determined. In addition to these analyses, the accuracy of the generated calibrations was assessed as the correlations between determined and observed phenotypic values. The calibration of plant height based on light curtain data achieved a technical repeatability of 0.99 and a correlation coefficient of 0.97, the calibration of dry matter content based on spectral imaging data a of 0.98 and a of 0.97. The generation and analysis of dry biomass calibrations revealed that a significant improvement of measurement accuracy can be achieved by a fusion of different sensors and data evaluations. The calibration of dry biomass based on data of the light curtains, laser distance sensors, 3D Time-of-Flight cameras and spectral imaging achieved a of 0.99 and a of 0.92. The achieved excellent results illustrate the suitability of the developed platform, the integrated sensors and the data analysis software to non-invasively measure small grain cereals under field conditions. The high utility of the platform for plant breeding as well as for genomic studies was illustrated by the measurement of a large population with a total of 647 doubled haploid triticale lines derived from four families that were grown in four environments. The phenotypic data was determined based on platform measurements and showed a very high heritability for dry biomass yield. The combination of these phenotypic data with a genomic approach enabled the identification of quantitative trait loci (QTL), i.e., chromosomal regions affecting this trait. Furthermore, the repeated measurements revealed that the accumulation of biomass is controlled by temporal genetic regulation. Taken together, the very high robustness of the system, the excellent calibration results and the high heritability of the phenotypic data determined based on platform measurements demonstrate the utility of the precision phenotyping platform for plant breeding and its enormous potential to widen the phenotyping bottleneck.Die stetig wachsende Weltbevölkerung, sich ändernde Ernährungsgewohnheiten hin zu vermehrtem Fleischkonsum in Schwellenländern, der stetig wachsende Energiebedarf sowie der Klimawandel stellen große Herausforderungen an die Landwirtschaft von morgen. Um eine gesicherte Lebensmittel- und Energieversorgung zu gewährleisten muss die landwirtschaftliche Produktion bis 2050 um 70% gesteigert werden, wobei 90% dieser Steigerung durch eine Erhöhung der Erträge auf bereits bestehenden landwirtschaftlichen Flächen erzielt werden muss. Diese erforderliche Ertragssteigerung ist eine der größten Herausforderungen für die weltweite Landwirtschaft und bedarf unter anderem einer effizienten Züchtung neuer, an den Klimawandel angepasster, ertragsreicherer Sorten. Um eine ausreichende Steigerung der Erträge sicherstellen zu können müssen neue Methoden in der Pflanzenzucht etabliert werden, welche auf einer effizienten Geno- sowie Phänotypisierung der Pflanzen basieren. Im Bereich der Genotypisierung gab es in den letzten Jahrzehnten große Fortschritte, wodurch ein enormer Wissenszuwachs über die molekulare Basis komplexer Merkmale erzielt werden konnte. Trotzdem ist der Bereich der Phänotypisierung als ebenso wichtig anzusehen, da genetische Untersuchungen unter anderem von der Qualität phänotypischer Daten abhängen und qualitativ hochwertige phänotypische Daten die Selektion überlegener Linien in der Pflanzenzucht verbessern können. Im Vergleich zur Genotypisierung gab es jedoch im Bereich der Phänotypisierung in den letzten Jahrzehnten nur wenig wissenschaftlichen Fortschritt. Durch dieses Missverhältnis zwischen der Qualität phänotypischer und genotypischer Informationen bleibt somit ein erhebliches Potential an neuen Erkenntnissen unentdeckt. Das Ziel dieser Arbeit war die Entwicklung und Bewertung einer Präzisionsphänotypisierungsplattform zur zerstörungsfreien Charakterisierung von Energiegetreide in der Pflanzenzucht, um den aktuell bestehenden Flaschenhals bei der Umsetzung neuer Zuchtmethoden zu weiten. Die entwickelte Plattform ist ein Gespann bestehend aus einem Hochradschlepper mit 80 cm Bodenfreiheit, einem eigens entwickelten Trägeranhänger und einem am Trägeranhänger befestigten Sensormodul. Die innovative Sensorik zur Pflanzenvermessung, bestehend aus mehreren 3D Time-of-Flight Kameras, Laserabstandssensoren, Lichtgittern und einem bildgebenden Spektralmessgerät im nahen infrarot (NIR) Bereich, sowie die gesamte Systemtechnik zur Datenaufnahme wurden vollständig im Sensormodul integriert. Zur Bedienung des Systems wurde eine Software mit graphischer Benutzeroberfläche entwickelt, die eine zeit- und ortsbezogene Aufnahme der Sensorrohdaten ermöglicht, was die Grundlage einer anschließenden Sensor- und Datenfusion zur Merkmalsbestimmung darstellt. Zur Datenauswertung wurde eine Software mit graphischer Benutzeroberfläche unter Matlab entwickelt. Durch diese Software werden alle erstellten Sensormodelle und Algorithmen zur Datenauswertung auf die Rohdaten angewendet, wobei neue Algorithmen flexibel in das System eingebunden, Sensorfusionsmodelle erzeugt und kalibriert und Pflanzenparameter bestimmt werden können. Die entwickelte Plattform ermöglicht die simultane Vermessung mehrerer Pflanzenparameter bei einem Durchsatz von über 2000 Parzellen pro Tag. Basierend auf Daten aus den Jahren 2011 und 2012 wurden umfangreiche Kalibrierungen für die Parameter Pflanzenhöhe, Trockensubstanzgehalt und Trockenmasse für Triticale erstellt. Zu diesem Zweck wurden in beiden Jahren Feldversuche mit jeweils 600 Parzellen angelegt, doppelt mit der Plattform vermessen und zur Referenzwertgenerierung im Anschluss konventionell phänotypisiert. In beiden Jahren wurden drei Messungen von jeweils 200 Parzellen zu drei verschiedenen Zeitpunkten durchgeführt, um Daten unterschiedlicher Entwicklungsstadien der Pflanzen für die Erstellung der Kalibrierungen zur Verfügung zu haben. Zur Validierung der Rohdatenqualität sowie der Robustheit der Datenreduktionsverfahren wurden zunächst für alle entwickelten Auswertungsalgorithmen basierend auf den Wiederholungsmessungen die technischen Wiederholbarkeiten bestimmt. Neben der Validierung der Rohdatenqualität wurden die Genauigkeiten der erstellten Kalibrierungen als Korrelation zwischen den Referenzwerten und den mit der Sensorplattform gemessenen Werten ermittelt. Die Kalibrierung der Pflanzenhöhe basierend auf Lichtgitterdaten erreicht eine technische Wiederholbarkeit Rw2 von 0.99 und einen Korrelationskoeffizienten Rc² von 0.97, die Kalibrierung des Trockensubstanzgehalts basierend auf Spectral-Imaging Daten ein Rw2 von 0.98 und ein Rc² von 0.97. Bei der Erstellung der Trockenmasse Kalibrierung konnte gezeigt werden, dass durch eine Fusion verschiedener Sensoren und Datenauswertungen eine signifikante Verbesserung der Messgenauigkeit erreicht werden kann. Die Kalibrierung der Trockenmasse basierend auf Daten der Lichtgitter, Laserabstandssensoren, 3D Time-of-Flight Kameras und des Spectral-Imaging erreicht ein Rw2 von 0.99 und ein Rc² von 0.92. Die hervorragenden technischen Wiederholbarkeiten, sowie die exzellenten Genauigkeiten der entwickelten Kalibrierungen verdeutlichen die herausragende Eignung der entwickelten Plattform, der integrierten Sensoren und der entwickelten Datenaufnahme- sowie Datenauswertesoftware zur zerstörungsfreien Phänotypisierung von Getreide unter Feldbedingungen. Der hohe praktische Nutzen der Plattform für die Pflanzenzucht sowie für genetische Studien konnte durch die wiederholte Phänotypisierung einer DH Population mit 647 doppelhaploiden Triticale Linien in vier Umwelten aufgezeigt werden. Die Pflanzen wurden mit der Plattform an drei verschiedenen Zeitpunkten phänotypisiert und die erzeugten Daten zeigten eine sehr hohe Heritabilität für Biomasse. Die Kombination dieser phänotypischen mit genotypischen Informationen in einer Assoziationskartierungsstudie ermöglichte die Identifizierung von Regionen im Genom welche für quantitative Merkmale (QTL) kodieren. So konnten z.B. Regionen auf mehreren Chromosomen identifiziert werden, welche die Biomasse beeinflussen. Des Weiteren konnte durch Auswertung der wiederholten Messungen der Nachweis erbracht werden, dass die Biomasseentwicklung durch sich zeitlich ändernde genetische Mechanismen beeinflusst wird. Die erreichte sehr hohe Robustheit des Systems, die exzellenten Kalibrierungsergebnisse und die hohen Heritabilitäten der mit der Plattform bestimmten phänotypischen Daten verdeutlichen die hervorragende Eignung des Systems zur Anwendung in der Pflanzenzucht und das enorme Potential der entwickelten Technologie zur Weitung des aktuell bestehenden Phänotypisierungs-Flaschenhalses

Digital process management for the integrated bending of thermoplastic CFRP tapes

A bending based approach for the preforming of carbon fiber reinforced thermoplastic (CFRTP) tapes has been implemented at wbk Institute of Production Science. In early experimentation, it became evident that the process requires regular adaption of process parameters in order to meet the desired quality level. To limit the manual effort, the parameter variation and process characterization should be automated. In this paper, an approach for the automatic parameter identification and tuning through systematic variation combined with optical process measurement is presented. The functionality of the measurement system as well as the parameter identification and error compensation are described

Image segmentation and robust edge detection for collision avoidance in machine tools

Collisions are a major cause of unplanned downtime in small series manufacturing with machine tools. Existing solutions based on geometric simulation do not cover collisions due to setup errors. Therefore a solution is developed to compare camera images of the setup with the simulation, thus detecting discrepancies. The comparison focuses on the product being manufactured (workpiece) and the fixture holding the workpiece, thus the first step consists in segmenting the corresponding region of interest in the image. Subsequently edge detection is applied to the image to extract the relevant contours. Additional processing steps in the spatial and frequency domain are used to alleviate effects of the harsh conditions in the machine, including swarf, fluids and sub-optimal illumination. The comparison of the processed images with the simulation will be presented in a future publication

Visual-INS Using a Human Operator and Converted Measurements

A method human operated INS aiding is explored in which the pilot identifies and tracks a ground feature of unknown position over a short measurement epoch using an E/O sensor. One then refers to Visual-INS. In contrast to current research trends, a human operator is entrusted with visually tracking the ground feature. In addition, a less conventional measurement linearization technique is applied to generate “converted” measurements. A linear regression algorithm is then applied to the converted measurements providing an estimate of the INS horizontal velocity error and accelerometer biases. At the completion of the measurement epoch, the INS is corrected by subtracting out the estimated errors. Aiding the INS in this manner provides a significant improvement in the accuracy of the INS-provided aircraft navigation state estimates when compared to those of a free/unaided INS. A number of scenario are simulated including with and without a constrained flight path, with single vs. multiple ground feature tracking sessions, and with a navigation vs. tactical grade INS. Applications for this autonomous navigation approach include navigation in GPS denied environments and/or when RF emitting/receiving sensors are undesirable

The Impact of Training and Technology on the Future of Aviation

The comprehensive exam proposal is intended to address the core competency requirements for the degree of Master of Aeronautical Science. In addition, the proposal will address the competency requirements for the Aviation Education Specialization. In partial fulfillment of these requirements, the proposal will examine human factors as they pertain specifically to the arena of unmanned flight, the impact computer based training and web based training advances have and will continue to have on the aviation community, the technological, social, environmental, and political aspects of the air cargo industry as they pertain to the industry’s survival, the ability of Next Generation (NextGen) air traffic control technologies to navigate the advances in the aviation community and finally the ability of crew resource management to adapt and thrive in the ever advancing world of aviation technologies. The examination of these issues in aviation will be conducted utilizing a mixed-methodology. Qualitative and quantitative data will be analyzed ex-post facto for triangulation which will lead to the validity of conclusions

Multihop Rendezvous Algorithm for Frequency Hopping Cognitive Radio Networks

Cognitive radios allow the possibility of increasing utilization of the wireless spectrum, but because of their dynamic access nature require new techniques for establishing and joining networks, these are known as rendezvous. Existing rendezvous algorithms assume that rendezvous can be completed in a single round or hop of time. However, cognitive radio networks utilizing frequency hopping that is too fast for synchronization packets to be exchanged in a single hop require a rendezvous algorithm that supports multiple hop rendezvous. We propose the Multiple Hop (MH) rendezvous algorithm based on a pre-shared sequence of random numbers, bounded timing differences, and similar channel lists to successfully match a percentage of hops. It is tested in simulation against other well known rendezvous algorithms and implemented in GNU Radio for the HackRF One. We found from the results of our simulation testing that at 100 hops per second the MH algorithm is faster than other tested algorithms at 50 or more channels with timing ±50 milliseconds, at 250 or more channels with timing ±500 milliseconds, and at 2000 channels with timing ±5000 milliseconds. In an asymmetric environment with 100 hops per second, a 500 millisecond timing difference, and 1000 channels the MH algorithm was faster than other tested algorithms as long as the channel overlap was 35% or higher for a 50% required packet success to complete rendezvous. We recommend the Multihop algorithm for use cases with a fast frequency hop rate and a slow data transmission rate requiring multiple hops to rendezvous or use cases where the channel count equals or exceeds 250 channels, as long as timing data is available and all of the radios to be connected to the network can be pre-loaded with a shared seed

Motion Generation and Planning System for a Virtual Reality Motion Simulator: Development, Integration, and Analysis

In the past five years, the advent of virtual reality devices has significantly influenced research in the field of immersion in a virtual world. In addition to the visual input, the motion cues play a vital role in the sense of presence and the factor of engagement in a virtual environment. This thesis aims to develop a motion generation and planning system for the SP7 motion simulator. SP7 is a parallel robotic manipulator in a 6RSS-R configuration. The motion generation system must be able to produce accurate motion data that matches the visual and audio signals. In this research, two different system workflows have been developed, the first for creating custom visual, audio, and motion cues, while the second for extracting the required motion data from an existing game or simulation. Motion data from the motion generation system are not bounded, while motion simulator movements are limited. The motion planning system commonly known as the motion cueing algorithm is used to create an effective illusion within the limited capabilities of the motion platform. Appropriate and effective motion cues could be achieved by a proper understanding of the perception of human motion, in particular the functioning of the vestibular system. A classical motion cueing has been developed using the model of the semi-circular canal and otoliths. A procedural implementation of the motion cueing algorithm has been described in this thesis. We have integrated all components together to make this robotic mechanism into a VR motion simulator. In general, the performance of the motion simulator is measured by the quality of the motion perceived on the platform by the user. As a result, a novel methodology for the systematic subjective evaluation of the SP7 with a pool of juries was developed to check the quality of motion perception. Based on the results of the evaluation, key issues related to the current configuration of the SP7 have been identified. Minor issues were rectified on the flow, so they were not extensively reported in this thesis. Two major issues have been addressed extensively, namely the parameter tuning of the motion cueing algorithm and the motion compensation of the visual signal in virtual reality devices. The first issue was resolved by developing a tuning strategy with an abstraction layer concept derived from the outcome of the novel technique for the objective assessment of the motion cueing algorithm. The origin of the second problem was found to be a calibration problem of the Vive lighthouse tracking system. So, a thorough experimental study was performed to obtain the optimal calibrated environment. This was achieved by benchmarking the dynamic position tracking performance of the Vive lighthouse tracking system using an industrial serial robot as a ground truth system. With the resolution of the identified issues, a general-purpose virtual reality motion simulator has been developed that is capable of creating custom visual, audio, and motion cues and of executing motion planning for a robotic manipulator with a human motion perception constraint