Dissertations / Theses on the topic 'Crop Phenotyping'

Doctor of Philosophy
Department of Agronomy
V. P. Vara Prasad
Crop assessment and monitoring is important to crop management both at crop production level and research plot level, such as high-throughput phenotyping in breeding programs. Optical sensors based agricultural applications have been around for decades and have soared over the past ten years because of the potential of some new technologies to be low-cost, accessible, and high resolution for crop remote sensing which can help to improve crop management to maintain producers’ income and diminish environmental degradation. The overall objective of this study was to develop methods and compare the different optical sensors in crop assessment and monitoring at different scales and perspectives. At crop production level, we reviewed the current status of different optical sensors used in precision crop production including satellite-based, manned aerial vehicle (MAV)-based, unmanned aircraft system (UAS)-based, and vehicle-based active or passive optical sensors. These types of sensors were compared thoroughly on their specification, data collection efficiency, data availability, applications and limitation, economics, and adoption. At research plot level, four winter wheat experiments were conducted to compare three optical sensors (a Canon T4i® modified color infrared (CIR) camera, a MicaSense RedEdge® multispectral imager and a Holland Scientific® RapidScan CS-45® hand-held active optical sensor (AOS)) based high-throughput phenotyping for in-season biomass estimation, canopy estimation, and grain yield prediction in winter wheat across eleven Feekes stages from 3 through 11.3. The results showed that the vegetation indices (VIs) derived from the Canon T4i CIR camera and the RedEdge multispectral camera were highly correlated and can equally estimate winter wheat in-season biomass between Feekes 3 and 11.1 with the optimum point at booting stage and can predict grain yield as early as Feekes 7. Compared to passive sensors, the RapidScan AOS was less powerful and less temporally stable for biomass estimation and yield prediction. Precise canopy height maps were generated from a CMOS sensor camera and a multispectral imager although the accuracy could still be improved. Besides, an image processing workflow and a radiometric calibration method were developed for UAS based imagery data as bi-products in this project. At temporal dimension, a wheat phenology model based on weather data and field contextual information was developed to predict the starting date of three key growth stages (Feekes 4, 7, and 9), which are critical for N management. The model could be applied to new data within the state of Kansas to optimize the date for optical sensor (such as UAS) data collection and save random or unnecessary field trips. Sensor data collected at these stages could then be plugged into pre-built biomass estimation models (mentioned in the last paragraph) to estimate the productivity variability within 20% relative error.

Doctor of Philosophy
Genetics Interdepartmental Program - Plant Pathology
Jesse A. Poland
A major challenge for 21st century plant geneticists is to predict plant performance based on genetic information. This is a daunting challenge, especially when there are thousands of genes that control complex traits as well as the extreme variation that results from the environment where plants are grown. Rapid advances in technology are assisting in overcoming the obstacle of connecting the genotype to phenotype. Next generation sequencing has provided a wealth of genomic information resulting in numerous completely sequenced genomes and the ability to quickly genotype thousands of individuals. The ability to pair the dense genotypic data with phenotypic data, the observed plant performance, will culminate in successfully predicting cultivar performance. While genomics has advanced rapidly, phenomics, the science and ability to measure plant phenotypes, has slowly progressed, resulting in an imbalance of genotypic to phenotypic data. The disproportion of high-throughput phenotyping (HTP) data is a bottleneck to many genetic and association mapping studies as well as genomic selection (GS). To alleviate the phenomics bottleneck, an affordable and portable phenotyping platform, Phenocart, was developed and evaluated. The Phenocart was capable of taking multiple types of georeferenced measurements including normalized difference vegetation index and canopy temperature, throughout the growing season. The Phenocart performed as well as existing manual measurements while increasing the amount of data exponentially. The deluge of phenotypic data offered opportunities to evaluate lines at specific time points, as well as combining data throughout the season to assess for genotypic differences. Finally in an effort to predict crop performance, the phenotypic data was used in GS models. The models combined molecular marker data from genotyping-by-sequencing with high-throughput phenotyping for plant phenotypic characterization. Utilizing HTP data, rather than just the often measured yield, increased the accuracy of GS models. Achieving the goal of connecting genotype to phenotype has direct impact on plant breeding by allowing selection of higher yielding crops as well as selecting crops that are adapted to local environments. This will allow for a faster rate of improvement in crops, which is imperative to meet the growing global population demand for plant products.

Despite the success of breeding for high-yielding varieties during and since the ‘Green Revolution’, there are still an ever increasing number of people who suffer from malnutrition, due to both inadequate calorie intake and ‘hidden hunger’ from insufficient essential nutrients. There are also adverse impacts of such high-input, intensive agriculture on the wider environment. It is necessary therefore to focus breeding efforts on improving nutrient uptake and composition of crops, as well as improved yield. Roots have been an under-utilised focus of crop breeding, because of difficulty in observation and accurate measurement. Furthermore, genetic diversity in crop roots may have been lost in commercial varieties because of the focus on above-ground traits and the use of fertilisers. Techniques which can accurately measure phenotypic variation in roots, of a diverse range of germplasm at a high throughput, would increase the potential for identifying novel genetic loci related to improved nutrient uptake and composition. The aim of this PhD was to screen at high throughput in a controlled-environment, the roots of an array of Brassica napus germplasm. The validity of the system to predict field performance, in traits including early vigour, nutrient composition and yield was assessed. Genetic loci underlying variation for the root traits were also investigated. A high throughput screen of the mineral composition of a mutagenised B. rapa population was also conducted, with the aim of identifying mutants with enhanced mineral composition of human essential elements, particularly magnesium. It has been demonstrated that root traits in the high throughput system can predict field performance, particularly primary root length which has the greatest ‘broad-sense heritability’ and relates to early vigour and yield. Lateral root density on the otherhand was found across the studies to relate to mineral composition, particularly of micro-nutrients. Genetic loci underlying root traits, and enhanced magnesium accumulation have been identified, and have potential for use in breeding Brassica with improved mineral nutrition.

Fusarium Head Blight (FHB) is a destructive disease caused by Fusarium graminearum that affects wheat (Triticum aestivum L.) worldwide. Breeding for resistance to FHB is arguably the best way to combat this disease. However, FHB resistance is highly complex and phenotypic screening is difficult. Molecular markers are a promising tool but breeding programs face the challenge of allocating resources in such a way that the optimum balance between phenotypic and genotypic selection is reached. An F2:3 population derived from a resistant x susceptible cross was subjected to phenotypic and genotypic selection. For phenotyping, a novel air separation method was used to measure percentage of damaged kernels (FDK). Heritability estimates were remarkably high, which was attributed to the type of cross and the quality of phenotyping. Genotypic selection was done by selecting resistance alleles at quantitative trait loci (QTL) on the 3BS (Fhb1) and the 2DL chromosomes. Fhb1 conferred a moderate but stable FHB resistance while the 2DL QTL conferred a surprisingly high level of resistance but with significant interaction with the environment. Phenotypic selection conferred higher or lower genetic gains than genotypic selection, depending on the selection intensity. Based on these results, different selection strategies are discussed.

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.
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.

Most staple food crops were domesticated thousands of years ago through independent processes across different regions of the world. Studies of the history of such crops have been essential to our understanding of plant domestication as a process that started with the collection of wild material and continued with subsequent propagation, cultivation, and selection under human care. Domestication often involves a complex genetic structure with contributions from multiple founder populations, interspecific hybridization, chromosomal introgressions, and polyploidization events that occurred hundreds to thousands of years earlier. Such intricate origins complicate the systematic study of the sources of phenotypic variation. The analysis of recently domesticated, non-traditional, non-model species, such as Sinningia speciosa (Gesneriaceae), can expand the knowledge that we have on phenotypic variation under domestication, and help us to comprehend modern patterns of plant domestication and to broaden our understanding of the general trends. S. speciosa is commonly known as the 'florist's gloxinia', and it has been cultivated for 200 years as an ornamental houseplant. In our genomic study of S. speciosa, we examined an extensive diversity panel consisting of 115 individuals that included different species in the genus, wild representatives, and cultivated accessions, as well as 150 individuals from an F2 segregating population. Our analyses revealed that all of the domesticated varieties are derived from a single founder population that originated in or near the city of Rio de Janeiro in Brazil. We identified two loci associated with domesticated traits (flower symmetry and color) and did not detect any major hybridization or polyploidization events that could have contributed to the rapid increase in phenotypic diversity. Our findings, in conjunction with other features such as a small, low-complexity genome, ease of cultivation, and rapid generation time, makes this species an attractive model for the study of genomic variation under domestication. Basic research on non-model organisms with low economic importance is uncommon but necessary to understand the world from a broader perspective. In such cases, reduced representation approaches like Genotyping-by-Sequencing (GBS) are efficient low-cost alternatives to whole genome resequencing. However, most of these technologies are subject to patent protection, licensing processes, and fees that constrain genomic research for small non-profit research organizations. We have designed a protocol to construct reduced representation libraries from genomic DNA. Our approach, called Targeted Amplification of Scattered Sites (TASS), deviates from the traditional digestion-ligation-amplification process that is the subject of intellectual property that protects most current methods. Instead, TASS relies on 1) targeting and duplicating scattered regions in the genome by annealing and expanding long tail primers with short annealing sites, and 2) amplifying these regions using primers that are complementary to the added overhangs. At the moment GBS is more consistent and delivers more variants than TASS. However, we have established a foundation on which further optimization can produce an accessible, easy to implement, high-throughput genotyping approach.
Doctor of Philosophy
Most staple food crops were domesticated thousands of years ago through unrelated processes that were initiated across different regions of the world. Studies of the history of such crops have been essential to our understanding of plant domestication, a process that started with the collection of wild material and continued with subsequent propagation and cultivation under human care. Plant domestication has often involved a complex combination of ancestral lineages that encompass multiple populations, crosses with other species, and large DNA reorganizations that occurred hundreds to thousands of years earlier. Such intricate origins make the systematic study of plant domestication very challenging. The analysis of recently domesticated plants such as the 'florist's gloxinia' (Sinningia speciosa), can help us to better understand some of the changes that have occurred during domestication, as well as to comprehend modern patterns of plant domestication and to broaden our understanding of general trends. Florist's gloxinias are ornamental plants that have been cultivated during the last 200 years. In this study we examined 115 specimens, including wild and cultivated types of florist's gloxinias, as well as closely related species in Sinningia. We also constructed and evaluated an artificial population of 150 individuals from the cross of a wild and a cultivated form. Our analyses revealed that all of the domesticated varieties are descendants from a single wild population that originated in or near the city of Rio de Janeiro in Brazil. We also identified two regions of DNA that are responsible for the changes in flower shape and color, and crosses with other species did not introduce such alterations. Our findings, in conjunction with other features such as its small nuclear DNA content, the ease of cultivation indoors, and a rapid generation time, makes the florists' gloxinia an attractive crop to the study the effects of plant domestication. Research on organisms with low economic importance is uncommon but necessary to understand the world from a broader perspective. In such cases, analyzing the entire genetic information that is stored as DNA may be cost-prohibitive. Instead, approaches that sample small portions of DNA from each individual can be utilized. Most of these technologies are currently patented and subject to licensing processes and fees that limit their implementation by small non-profit research organizations. In this study we designed a protocol to sample small portions of DNA, similarly to existing techniques. However, our approach, called Targeted Amplification of Scattered Sites (TASS), employs a sampling process that deviates from the traditional patented procedure that is used in most current methods. At present, TASS is not as consistent and delivers less information than traditional approaches. However, we have established a foundation on which further optimization can produce an accessible and easy to implement technique.

World cereal stocks need to increase in order to meet growing demands. Currently, maize, rice, wheat, are the main crops worldwide, while other cereals such as barley, sorghum, oat or different millets are also well placed in the top list. Crop productivity is affected directly by climate change factors such as heat, drought, floods or storms. Researchers agree that global climate change is having a major impact on crop productivity. In that way, several studies have been focused on climate change scenarios and more specifically abiotic stresses in cereals. For instance, in the case of heat stress, high temperatures between anthesis to grain filling can decrease grain yield. In order to deal with the climate change and future environmental scenarios, plant breeding is one of the main alternatives breeding is even considered to contribute to the larger component of yield growth compared to management. Plant breeding programs are focused on identifying genotypes with high yields and quality to act as a parentals and further the best individuals among the segregating population thus develop new varieties of plants. Breeders use the phenotypic data, plant and crop performance, and genetic information to improve the yield by selection (GxE, with G and E indicating genetic and environmental factors). More factors must be taken into account to increase the yield, such as, for instance, the education of farmers, economic incentives and the use of new technologies (GxExM, with M indicating management). Plant phenotyping is related with the observable (or measurable) characteristics of the plant while the crop growing as well as the association between the plant genetic background and its response to the environment (GxE). In traditional phenotyping the measurements are collated manually, which is tedious, time consuming and prone to subjective errors. Nowadays the technology is involved in many applications. From the point of view of plan phenotyping, technology has been incorporated as a tool. The use of image processing techniques integrating sensors and algorithm processes, is therefore, an alternative to asses automatically (or semi-automatically) these traits. Images have become a useful tool for plant phenotyping because most frequently data from the sensors are processed and analyzed as an image in two (2D) or three (3D) dimensions. An image is the arrangement of pixels in a regular Cartesian coordinates as a matrix, each pixel has a numerical value into the matrix which represents the number of photons captured by the sensor within the exposition time. Therefore, an image is the optical representation of the object illuminated by a radiating source. The main characteristics of images can be defined by the sensor spectral and spatial properties, with the spatial properties of the resulting image also heavily dependent on the sensor platform (which determines the distance from the target object).
Las existencias mundiales de cereales deben aumentar para satisfacer la creciente demanda. Actualmente, el maíz, el arroz y el trigo son los principales cultivos a nivel mundial, otros cereales como la cebada, el sorgo y la avena están también bien ubicados en la lista. La productividad de los cultivos se ve afectada directamente por factores del cambio climático como el calor, la sequía, las inundaciones o las tormentas. Los investigadores coinciden en que el cambio climático global está teniendo un gran impacto en la productividad de los cultivos. Es por esto que muchos estudios se han centrado en escenarios de cambio climático y más específicamente en estrés abiótico. Por ejemplo, en el caso de estrés por calor, las altas temperaturas entre antesis y llenado de grano pueden disminuir el rendimiento del grano. Para hacer frente al cambio climático y escenarios ambientales futuros, el mejoramiento de plantas es una de las principales alternativas; incluso se considera que las técnicas de mejoramiento contribuyen en mayor medida al aumento del rendimiento que el manejo del cultivo. Los programas de mejora se centran en identificar genotipos con altos rendimientos y calidad para actuar como progenitores y promover los mejores individuos para desarrollar nuevas variedades de plantas. Los mejoradores utilizan los datos fenotípicos, el desempeño de las plantas y los cultivos, y la información genética para mejorar el rendimiento mediante selección (GxE, donde G y E indican factores genéticos y ambientales). El fenotipado plantas está relacionado con las características observables (o medibles) de la planta mientras crece el cultivo, así como con la asociación entre el fondo genético de la planta y su respuesta al medio ambiente (GxE). En el fenotipado tradicional, las mediciones se clasifican manualmente, lo cual es tedioso, consume mucho tiempo y es propenso a errores subjetivos. Sin embargo, hoy en día la tecnología está involucrada en muchas aplicaciones. Desde el punto de vista del fenotipado de plantas, la tecnología se ha incorporado como una herramienta. El uso de técnicas de procesamiento de imágenes que integran sensores y algoritmos son por lo tanto una alternativa para evaluar automáticamente (o semiautomáticamente) estas características.

Agriculture faces many challenges related to the increasing food demands of a growing global population and the sustainable use of resources in a changing environment. To address them, we need reliable information sources, like exploiting hyperspectral satellite, airborne, and ground-based remote sensing data to observe phenological traits through a crops growth cycle and gather information to precisely diagnose when, why, and where a crop is suffering negative impacts. By combining hyperspectral capabilities with unmanned aerial vehicles (UAVs), there is an increased capacity for providing time-critical monitoring and new insights into patterns of crop development. However, considerable effort is required to effectively utilize UAV-integrated hyperspectral systems in crop-modeling and crop-breeding tasks. Here, a UAV-based hyperspectral solution for mapping crop physiological parameters was explored within a machine learning framework. To do this, a range of complementary measurements were collected from a field-based phenotyping experiment, based on a diversity panel of wild tomato (Solanum pimpinellifolium) that were grown under fresh and saline conditions. From the UAV data, positionally accurate reflectance retrievals were produced using a computationally robust automated georectification and mosaicking methodology. The resulting multitemporal UAV data were then employed to retrieve leaf-chlorophyll (Chl) dynamics via a machine learning framework. Several approaches were evaluated to identify the best-performing regression supervised methods. An investigation of two learning strategies (i.e., sequential and retraining) and the value of using spectral bands and vegetation indices (VIs) as prediction features was also performed. Finally, the utility of UAVbased hyperspectral phenotyping was demonstrated by detecting the effects of salt-stress on the different tomato accessions by estimating the salt-induced senescence index from the retrieved Chl dynamics, facilitating the identification of salt-tolerant candidates for future investigations. This research illustrates the potential of UAV-based hyperspectral imaging for plant phenotyping and precision agriculture. In particular, a) developing systematic imaging calibration and pre-processing workflows; b) exploring machine learning-driven tools for retrieving plant phenological dynamics; c) establishing a plant stress detection approach from hyperspectral-derived metrics; and d) providing new insights into using computer vision, big-data analytics, and modeling strategies to deal effectively with the complexity of the UAV-based hyperspectral data in mapping plant physiological indicators.

The presented work quantifies the impacts of inundated land area (ILA) on streamflow and crop development in the Upper Midwest, which is experiencing a changing climate with observed increases in temperature and precipitation. Quantitative information is needed to understand how upland and downstream stakeholders are impacted by ILA; yet the temporal and spatial extent of ILA and the impact of water storage on flood propagation is poorly understood. Excess water in low gradient agricultural landscapes resulting in ILA can have opposing impacts. The ILA can negatively impact crop development causing financial loss from a reduction or total loss in yield while conversely, ILA can also benefit downstream stakeholders by preventing flood damage from the temporary surface storage that slows water movement into channels. This research evaluates the effects of ILA on streamflow and crop development by leveraging the utility of remotely sensed observations and models.

The influence of ILA on streamflow is investigated in the Red River basin, a predominantly agricultural basin with a history of damaging flood events. An inundation depth-area (IDA) parameterization was developed to parameterize the ILA in a hydrologic model, the Variable Infiltration Capacity (VIC) model, using remotely sensed observations from the MODIS Near Real-Time Global Flood Mapping product and discharge data. The IDA parameterization was developed in a subcatchment of the Red River basin and compared with simulation scenarios that did and did not represent ILA. The model performance of simulated discharge and ILA were evaluated, where the IDA parameterization outperformed the control scenarios. In addition, the simulation results using the IDA parameterization were able to explain the dominant runoff generation mechanism during the winter-spring and summer-fall seasons. The IDA parameterization was extended to the Red River basin to analyze the effects of ILA on the timing and magnitude of peak flow events where observed discharge revealed an increasing trend and magnitude of summer peak flow events. The results also showed that the occurrence of peak flow events is shifting from unimodal to bimodal structure, where peak flow events are dominant in the spring and summer seasons. By simulating ILA in the VIC model, the shift in occurrence of peak flow events and magnitude are better represented compared to simulations not representing ILA.

The impacts of ILA on crop development are investigated on soybean fields in west-central Indiana using proximal remote sensing from unmanned aerial systems (UASs). Models sensitive to ILA were developed from the in-situ and UAS data at the plot scale to estimate biomass and percent of expected yield between the R4-R6 stages at the field scale. Low estimates of biomass and percent of expected yield were associated with mapped observations of ILA. The estimated biomass and percent of expected yield were useful early indicators to identify soybean impacted by excess water at the field scale. The models were applied to satellite imagery to quantify the impacts of ILA on soybean development over larger areas and multiple years. The estimated biomass and percent of expected yield correlated well with the observed data, where low model estimates were also associated with mapped observations of ILA and periods of excessive rainfall. The results of the work link the impacts of ILA on streamflow and crop development, and why it is important to quantify both in a changing climate. By representing ILA in hydrologic models, we can improve simulated streamflow and ILA and represent dominant physical process that influence hydrologic responses and represent shift and seasonal occurrence of peak flow events. In the summer season, where there is an increased occurrence of peak flow events, it is important to understand the impacts of ILA on crop development. By quantifying the impacts of ILA on soybean development we can analyze the spatiotemporal impacts of excess water on soybean development and provide stakeholders with early assessments of expected yield which can help improvement management decisions.

Crop improvement is necessary for food security as the global population is expected to exceed 9 billion by 2050. Limitations in water resources and more frequent droughts and floods will make it increasingly difficult to manage agricultural resources and increase yields. Therefore, we must improve our ability to monitor agronomic research plots and use the information they provide to predict impacts of moisture stress on crop growth and yield. Towards this end, agronomists have used reductions in leaf expansion rates as a visible ‘plant-based’ indicator of moisture stress. Also, modeling researchers have developed crop models such as AquaCrop to enable quantification of the severity of moisture stress and its impacts on crop growth and yield. Finally, breeders are using Unmanned Aircraft Systems (UAS) in field-based High-Throughput Phenotyping (HTP) to quickly screen large numbers of small agronomic research plots for traits indicative of drought and flood tolerance. Here we investigate whether soybean canopy cover and color time series from high-resolution UAS ortho-images can be collected with enough spatial and temporal resolution to accurately quantify and differentiate agronomic research plots, pinpoint the timing of the onset of moisture stress, and constrain crop models such as AquaCrop to more accurately simulate the timing and severity of moisture stress as well as its impacts on crop growth and yield. We find that canopy cover time series derived from multilayer UAS image ortho-mosaics can reliably differentiate agronomic research plots and pinpoint the timing of reductions in soybean canopy expansion rates to within a couple of days. This information can be used to constrain the timing of the onset of moisture stress in AquaCrop resulting in a more realistic simulation of moisture stress and a lower likelihood of underestimating moisture stress and overestimating yield. These capabilities will help agronomists, crop modelers, and breeders more quickly develop varieties tolerant to moisture stress and achieve food security.

Comprehensive information of breeding germplasm is a necessity to develop effective strategies for accelerated breeding. I characterized Purdue University soft red winter wheat breeding population that was subjectof intensive germplasm introduction and introgression from exotic germplasm. Using genotyping-by-sequences (GBS) approach, I developed ~15,000 single nucleotide polymorphisms (SNPs) and studied extent of linkage disequilibrium (LD)and hidden population structure in the population.The extent of LD and its decay varied among chromosomes with chromosomes 2B and 7D showing the most extended islands of high-LDandslow rates of decay. Four sub-populations, two with North American origin and two with Australian and Chinese origins, were identified. Genome-wide scans for signatures of selection using FSTand hapFLK identified 13 genomic regions under selection, of which six loci (LT, Ppd-B1, Fr-A2, Vrn-A1, Vrn-B1, Vrn3) were associated with environmental adaptation and two loci were associated with disease resistance genes (Sr36 and Fhb1).

Unmanned aerial vehicles (UAV) have revolutionized data collection in large scale agronomic field trials (10+ ha). Vegetative index (VI) maps derived from UAV imagery are a potential tool to characterize temporal and spatial treatment effects in a more efficient and non-destructive way compared to traditional data collection methods that require manual sampling. The overall objective of this study was to characterize and quantify maize responses to experimental treatments in field-scale research using UAV imagery. The specific objectives were: 1) to assess the performance of several VI as predictors of grain yield and to evaluate their ability to distinguish between fertilizer treatments, and the effects of removing soil and shadow background, 2) to assess the performance of VI and canopy cover fraction (CCF) as predictors of maize biomass at vegetative and reproductive growth stages under field-scale conditions, and 3) to compare the performance of VI derived from consumer-grade and multispectral sensors for predicting grain yield and identifying treatment effects. For the first objective, the results suggest that most VI were good indicators of grain yield at late vegetative and early reproductive growth stages, and that removing soil background improved the characterization of maize responses to experimental treatments. For objective two, overall, CCF was the best to predict biomass at early vegetative growth stages, while VI at reproductive growth stages. Finally, for objective three, performance of consumer-grade and multispectral derived VI were similar for predicting grain yield and identifying treatment effects.

Вивчено феногенетичну структуру локальної популяції Leptinotarsa decemlineata Say за частотою різних феноформ і фенів пронотума та елітер імаго на рослинах картоплі, помідорів та перцю. Встановлено, що на характер поліморфізму рисунку пронотума і елітер імаго Leptinotarsa decemlineata Say потужно впливає харчовий фактор та «пестицидний стрес».
The phenogenetic structure of a local population of Leptinotarsa decemlineata Say has been studied based on the frequency of various phenological forms and phenes of pronotum and elytra of imagos found on plants of potato, tomatoe and pepper. It has been determined that the character of the polymorphism of pronotum and elytra pattern in imagos of Leptinotarsa decemlineata Say has been sufficiently influenced by the food factor and pesticidal stress.

Hyperspectral imaging has become one of the most popular technologies in plant phenotyping because it can efficiently and accurately predict numerous plant physiological features such as plant biomass, leaf moisture content, and chlorophyll content. Various hyperspectral imaging systems have been deployed in both greenhouse and field phenotyping activities. However, the hyperspectral imaging quality is severely affected by the continuously changing environmental conditions such as cloud cover, temperature and wind speed that induce noise in plant spectral data. Eliminating these environmental effects to improve imaging quality is critically important. In this thesis, two approaches were taken to address the imaging noise issue in greenhouse and field separately. First, a computational simulation model was built to simulate the greenhouse microclimate changes (such as the temperature and radiation distributions) through a 24-hour cycle in a research greenhouse. The simulated results were used to optimize the movement of an automated conveyor in the greenhouse: the plants were shuffled with the conveyor system with optimized frequency and distance to provide uniform growing conditions such as temperature and lighting intensity for each individual plant. The results showed the variance of the plants’ phenotyping feature measurements decreased significantly (i.e., by up to 83% in plant canopy size) in this conveyor greenhouse. Secondly, the environmental effects (i.e., sun radiation) on aerial hyperspectral images in field plant phenotyping were investigated and modeled. An artificial neural network (ANN) method was proposed to model the relationship between the image variation and environmental changes. Before the 2019 field test, a gantry system was designed and constructed to repeatedly collect time-series hyperspectral images with 2.5 minutes intervals of the corn plants under varying environmental conditions, which included sun radiation, solar zenith angle, diurnal time, humidity, temperature and wind speed. Over 8,000 hyperspectral images of corn (Zea mays L.) were collected with synchronized environmental data throughout the 2019 growing season. The models trained with the proposed ANN method were able to accurately predict the variations in imaging results (i.e., 82.3% for NDVI) caused by the changing environments. Thus, the ANN method can be used by remote sensing professionals to adjust or correct raw imaging data for changing environments to improve plant characterization.

Crop wild relatives (CWR) are an indispensable and at the same time threatened genetic resources for plant breeding. The study uses wild species related to celery to demonstrate how genetic resources of CWRs can be actively maintained in their natural surroundings (in-situ). Genetic reserves should be designated for long term conservation of selected occurrences. The study presents the selection procedure in detail, aiming at the identification of occurrences and sites suitable for the designation of genetic reserves, the spatial model of a genetic reserve and first practical results of the project. The overall aim of the project is the establishment of a nationwide network of genetic reserves for Apium graveolens, Helosciadium repens, H. nodiflorum and H. inundatum, the four wild celery species native to Germany. Helosciadum repens (Jacq.) W.D.J.Koch is threatened by genetic erosion due to a decline in population numbers and sizes. The loss of any population is an irretrievable loss of diversity and opportunity to enhance crops in the future. Genetic reserves are one way to conserve these populations and their genetic potential. Twenty-seven populations were selected for the analysis in a decision process based on site information. Microsatellites (SSR) were used to elucidate the genetic diversity of German populations. A cluster analysis was performed to see if the individuals form clusters of similarity. For that, a discriminate analysis of principal components (DAPC) was conducted, as the inbreeding index indicated a high number of inbreeding events in the populations and thus discordance with HWE (Hardy-Weinberg equilibrium). The analysis identified six genetic groups, which coincide well with the geographic origin of the analysed plants. The allelic richness (mean counts of alleles per individual per population) was higher in the southern populations compared to the northern ones. This North-South discrepancy was also visible as a high heterogeneity in the cluster assignments in the DAPC analysis. These differences in genetic diversity might be a result of the biogeographic history of Europe, especially the last glacial maximum. For the establishment of genetic reserves, two populations were considered as most important: The population that differs the most from the average genetic composition and the population that represents the average genetic composition of a population the best. The two extremes of differentiation were interpreted as such that the former has a specific adaptation to its local environment, and the latter represents all populations the best. DifferInt was used to analyse the SSR data and validate the differentiation of all populations compared to a pool of populations. However, SSRs are not capable of detecting adaptive traits. Populations were additionally chosen from different eco-geographic units (EGU), to increase the chance of capturing different traits. EGUs (Naturräume) are areas of specific abiotic and biotic features. These features may influence selection pressures and induce local adaptations. Based on site parameters and genetic data, 14 most appropriate wild populations (MAWP) were identified for genetic reserves establishment. For H. repens, two eco-forms are known and described in the literature. Besides their different habitats (terrestrial/semi-terrestrial and aquatic) they can be differentiated by morphological traits. Leave and stolon sizes and flowering behaviour differ significantly. Furthermore, the roots of the aquatic forms do not anchor in soil but on other aquatic plants, wood or roots of trees, while the terrestrial form exhibits a shallow root system network similar to other perennial species. To this end, no genetic analysis was conducted to clarify the phylogenetic status of the putative forms and authors avoided the usage of any specific noun rather than form. The SSR data from the previous study was evaluated, particularly with regards to the two forms. Additionally, an ISSR analysis was conducted, and the data was used to perform a PCA. There was no genetic clustering regarding the two forms neither in the SSR nor in the ISSR data. Nonetheless, the North-South discrepancy in the genetic diversity that was visible in the DAPC plot was confirmed in the PCA of the ISSR data. However, markers may fail to detect quantitative variation for adaptively important traits. As the most obvious difference in the two habitats was the water availability, the adaptation of both forms to drought stress was studied by measuring the relative water content of leaves, system water content and water loss during drought stress conditions. The stomatal index was measured for different water treatment levels. The results indicate that phenotypic plasticity rather than genotypic adaptation is responsible for different H. repens phenotypes.