High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Background: Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

Leveraging Image Analysis for High-Throughput Plant Phenotyping

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant’s phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field

Developing affordable high-throughput plant phenotyping methods for breeding of cereals and tuber crops

High-throughput plant phenotyping (HTPP) is a fast, accurate, and non-destructive process for evaluating plants' health and environmental adaptability. HTPP accelerates the identification of agronomic traits of interest, eliminates subjectivism (which is innate to humans), and facilitates the development of adapted genotypes. Current HTPP methods often rely on imaging sensors and computer vision both in the field and under controlled (indoor) conditions. However, their use is limited by the costs and complexity of the necessary instrumentation, data analysis tools, and software. This issue could be overcome by developing more cost-efficient and user-friendly methods that let breeders, farmers, and stakeholders access the benefits of HTPP. To assist such efforts, this thesis presents an ensemble of dedicated affordable phenotyping methods using RGB imaging for a range of key applications under controlled conditions.  The affordable Phenocave imaging system for use in controlled conditions was developed to facilitate studies on the effects of abiotic stresses by gathering data on important plant characteristics related to growth, yield, and adaptation to growing conditions and cultivation systems. Phenocave supports imaging sensors including visible (RGB), spectroscopic (multispectral and hyperspectral), and thermal imaging. Additionally, a pipeline for RGB image analysis was implemented as a plugin for the free and easy-to-use software ImageJ. This plugin has since proven to be an accurate alternative to conventional measurements that produces highly reproducible results. A subsequent study was conducted to evaluate the effects of heat and drought stress on plant growth and grain nutrient composition in wheat, an important staple cereal in Sweden. The effects of stress on plant growth were evaluated using image analysis, while stress-induced changes in the abundance of key plant compounds were evaluated by analyzing the nutrient composition of grains via chromatography. This led to the discovery of genotypes whose harvest quality remains stable under heat and drought stress. The next objective was to evaluate biotic stress; for this case, the effect of the fungal disease Fusarium head blight (FHB) that affects grain development in wheat was investigated. For this purpose, seed phenotyping parameters were used to determine the components and settings of a statistical model, which predicts the occurrence of FHB. The results reveal that grain morphology evaluations, such as length and width, were found to be significantly affected by the disease. Another study was carried out to estimate the disease severity of the common scab (CS) in potatoes, a widely popular food source. CS occurs on the tubers and reduces their visual appeal, significantly affecting their market value. Tubers were analyzed by a deep learning-based method to estimate disease lesion areas caused by CS. Results showed a high correlation between the predictions and expert visual scorings of the disease and proved to be a potential tool for the selection of genotypes that fulfill the market standards and resistance to CS. Both case studies highlight the role of imaging in plant health monitoring and its integration into the larger picture of plant health management.  The methods presented in this work are a starting point for bridging the gap between costs and accessibility to imaging technology. These are affordable and user-friendly resources for generating pivotal knowledge on plant development and genotype selection. In the future, image acquisition of all the methods can be integrated into the Phenocave system, potentially allowing for a more automated and efficient plant health monitoring process, leading to the identification of tolerant genotypes to biotic and abiotic stresses

Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology

Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Estimating Crop Stomatal Conductance Through High-Throughput Plant Phenotyping

During photosynthesis and transpiration, crops exchange carbon dioxide and water with the atmosphere through stomata. When a crop experiences water stress, stomata are closed to reducing water loss. However, the closing of stomata also negatively affects the photosynthetic efficiency of the crop and leads to lower yields. Stomatal conductance (gs) quantifies the degree of stomatal opening and closing by using the rate of gas exchange between the crop and the atmosphere, which helps to understand the water status of the crop for better irrigation management. Unfortunately, gs measurement typically requires contact measuring instruments and manual collection in the field, which is time-consuming and labor-intensive. Thus, this study estimates gs in two ways. Firstly, plant phenotypic data and weather information were used to estimate gs for various types of crops. The plant phenotypic data were extracted from images captured by a thermal infrared camera, a multispectral camera, and a visible and near-infrared spectrometer integrated on field phenotyping platform. Weather information was obtained from a field weather station. The random forest regression (RFR) model performed the best with R2 of 0.69 and RMSE of 0.135 mol*m-2 *s-1 , while the model using weather parameters alone had R2 of 0.58 and RMSE of 0.161, and the model using phenotypic data alone had R2 values of 0.59 and RMSE of 0.158 mol*m-2 *s-1 . The results indicated that there was a complementary relationship between plant phenotypic data and weather information in estimating gs. The second aspect of the study was to estimate maize and soybean gs directly from near-infrared, thermal-infrared and RGB (Red Green Blue) images collected by the same platform. The results showed that the convolutional neural network (CNN) model outperformed the other models with an R2 of 0.52. In addition, adding soil moisture as a variable to the model improved its accuracy, which decreased the RMSE from 0.147 to 0.137 mol*m-2 *s-1 . This study highlights the potential of estimating gs from remote sensing and field phenotyping platforms to help growers obtain information about the water status of crops and plan irrigation more efficiently. Advisor: Yufeng G

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

High-throughput field phenotyping in cereals and implications in plant ecophysiology

[eng] Global climate change effects on agroecosystems together with increasing world population is already threatening food security and endangering ecosystem stability. Meet global food demand with crops production under climate change scenario is the core challenge in plant research nowadays. Thus, there is an urgent need to better understand the underpinning mechanisms of plant acclimation to stress conditions contributing to obtain resilient crops. Also, it is essential to develop new methods in plant research that permit to better characterize non-destructively plant traits of interest. In this sense, the advance in plant phenotyping research by high throughput systems is key to overcome these challenges, while its verification in the field may clear doubts on its feasibility. To this aim, this thesis focused on wheat and secondarily on maize as study species as they make up the major staple crops worldwide. A large panoply of phenotyping methods was employed in these works, ranging from RGB and hyperspectral sensing to metabolomic characterization, besides of other more conventional traits. All research was performed with trials grown in the field and diverse stressor conditions representative of major constrains for plant growth and production were studied: water stress, nitrogen deficiency and disease stress. Our results demonstrated the great potential of leave-to-canopy color traits captured by RGB sensors for in-field phenotyping, as they were accurate and robust indicators of grain yield in wheat and maize under disease and nitrogen deficiency conditions and of leaf nitrogen concentration in maize. On the other hand, the characterization of the metabolome of wheat tissues contributed to elucidate the metabolic mechanisms triggered by water stress and their relationship with high yielding performance, providing some potential biomarkers for higher yields and stress adaptation. Spectroscopic studies in wheat highlighted that leaf dorsoventrality may affect more than water stress on the reflected spectrum and consequently the performance of the multispectral/hyperspectral approaches to assess yield or any other relevant phenotypic trait. Anatomy, pigments and water changes were responsible of reflectance differences and the existence of leaf-side-specific responses were discussed. Finally, the use of spectroscopy for the estimation of the metabolite profiles of wheat organs showed promising for many metabolites which could pave the way for a new generation phenotyping. We concluded that future phenotyping may benefit from these findings in both the low-cost and straightforward methods and the more complex and frontier technologies.[cat] Els efectes del canvi climàtic sobre els agro-ecosistemes i l’increment de la població mundial posa en risc la seguretat alimentària i l’estabilitat dels ecosistemes. Actualment, satisfer les demandes de producció d’aliments sota l’escenari del canvi climàtic és el repte central a la Biologia Vegetal. Per això, és indispensable entendre els mecanismes subjacents de l’aclimatació a l’estrès que permeten obtenir cultius resilients. També és precís desenvolupar nou mètodes de recerca que permetin caracteritzar de manera no destructiva els trets d’interès. L’avenç del fenotipat vegetal amb sistemes d’alt rendiment és clau per abordar aquests reptes. La present tesi s’enfoca en el blat i secundàriament en el panís com a espècies d’estudi ja que constitueixen els cultius bàsics arreu del món. Un ampli ventall de mètodes de fenotipat s’han utilitzat, des sensors RGB a híper-espectrals fins a la caracterització metabolòmica. La recerca s’ha dut a terme en assajos de camp i s’han avaluat diversos tipus d’estrès representatius de les majors limitacions pel creixement i producció vegetal: estrès hídric i biòtic i deficiència de nitrogen. Els resultats demostraren el gran potencial dels trets del color RGB (des de la planta a la capçada) pel fenotipat de camp, ja que foren indicadors precisos del rendiment a blat i panís sota condicions de malaltia i deficiència de nitrogen i de la concentració de nitrogen foliar a panís. La caracterització metabolòmica de teixits de blat contribuí a esbrinar els processos metabòlics endegats per l’estrès hídric i la seva relació amb comportament genotípic, proporcionant bio-marcadors potencials per rendiments més alts i l’adaptació a l’estrès. Estudis espectroscòpics en blat van demostrar que la dorsoventralitat pot afectar més que l’estrès hídric sobre l’espectre de reflectància i consegüentment sobre el comportament de les aproximacions multi/híper-espectrals per avaluar el rendiment i d’altres trets fenotípics com anatòmics i contingut de pigments. Finalment, l’ús de l’espectroscòpia per l’estimació del contingut metabòlic als teixits de blat resulta prometedor per molts metabòlits, la qual cosa obre les portes per a un fenotipat de nova generació. El fenotipat pot beneficiar-se d’aquestes troballes, tant en els mètodes de baix cost com de les tecnologies més sofisticades i d’avantguarda

Uumanned Aerial Vehicle Data Analysis For High-throughput Plant Phenotyping

The continuing population is placing unprecedented demands on worldwide crop yield production and quality. Improving genomic selection for breeding process is one essential aspect for solving this dilemma. Benefitted from the advances in high-throughput genotyping, researchers already gained better understanding of genetic traits. However, given the comparatively lower efficiency in current phenotyping technique, the significance of phenotypic traits has still not fully exploited in genomic selection. Therefore, improving HTPP efficiency has become an urgent task for researchers. As one of the platforms utilized for collecting HTPP data, unmanned aerial vehicle (UAV) allows high quality data to be collected within short time and by less labor. There are currently many options for customized UAV system on market; however, data analysis efficiency is still one limitation for the fully implementation of HTPP. To this end, the focus of this program was data analysis of UAV acquired data. The specific objectives were two-fold, one was to investigate statistical correlations between UAV derived phenotypic traits and manually measured sorghum biomass, nitrogen and chlorophyll content. Another was to conduct variable selection on the phenotypic parameters calculated from UAV derived vegetation index (VI) and plant height maps, aiming to find out the principal parameters that contribute most in explaining winter wheat grain yield. Corresponding, two studies were carried out. Good correlations between UAV-derived VI/plant height and sorghum biomass/nitrogen/chlorophyll in the first study suggested that UAV-based HTPP has great potential in facilitating genetic improvement. For the second study, variable selection results from the single-year data showed that plant height related parameters, especially from later season, contributed more in explaining grain yield. Advisor: Yeyin Sh