Дисертації з теми "Crop Phenotyping"
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Wang, Huan. "Crop assessment and monitoring using optical sensors." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38224.
Повний текст джерелаDepartment 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.
Crain, Jared Levi. "Leveraging the genomics revolution with high-throughput phenotyping for crop improvement of abiotic stresses." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/32566.
Повний текст джерелаGenetics 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.
Thomas, C. L. "High throughput phenotyping of root and shoot traits in Brassica to identify novel genetic loci for improved crop nutrition." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/43440/.
Повний текст джерелаMcNulty, Sarah Kristine. "Accelerated Crop Domestication through Identification of Phenotypic Characteristics of Taraxacum kok-saghyz Relevant to Rubber Yield." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574766469110561.
Повний текст джерелаCampbell, Lesley G. "Rapid evolution in a crop-weed complex (Raphanus spp.)." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1166549627.
Повний текст джерелаAgostinelli, Andres Mateo. "PHENOTYPIC AND GENOTYPIC SELECTION FOR HEAD SCAB RESISTANCE IN WHEAT." UKnowledge, 2009. http://uknowledge.uky.edu/gradschool_theses/582.
Повний текст джерелаVergara, Díaz Omar. "High-throughput field phenotyping in cereals and implications in plant ecophysiology." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/668314.
Повний текст джерелаEls 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.
Hasing, Rodriguez Tomas Nestor. "Genomic Reconstruction of the Domestication History of Sinningia speciosa (Lodd.) Hiern, and the Development of a Novel Genotyping Approach." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/95510.
Повний текст джерелаDoctor 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.
Damesa, Tigist Mideksa [Verfasser], and Hans-Peter [Akademischer Betreuer] Piepho. "Weighting methods for variance heterogeneity in phenotypic and genomic data analysis for crop breeding / Tigist Mideksa Damesa ; Betreuer: Hans-Peter Piepho." Hohenheim : Kommunikations-, Informations- und Medienzentrum der Universität Hohenheim, 2019. http://d-nb.info/1199440035/34.
Повний текст джерелаFernández, Gallego José Armando. "Image processing techniques for plant phenotyping using RGB and thermal imagery = Técnicas de procesamiento de imágenes RGB y térmicas como herramienta para fenotipado de cultivos." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/669111.
Повний текст джерелаLas 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.
Kost, Matthew. "Maize and Sunflower of North America: Conservation and Utilization of Genetic Diversity." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408642177.
Повний текст джерелаAngel, Yoseline. "Monitoring crop development and health using UAV-based hyperspectral imagery and machine learning." Diss., 2021. http://hdl.handle.net/10754/670149.
Повний текст джерела(10292588), Stuart D. Smith. "Quantifying the impacts of inundated land area on streamflow and crop development." Thesis, 2021.
Знайти повний текст джерелаThe 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.
(6620090), Anthony A. Hearst. "Remote Sensing of Soybean Canopy Cover, Color, and Visible Indicators of Moisture Stress Using Imagery from Unmanned Aircraft Systems." Thesis, 2019.
Знайти повний текст джерела(8797730), Rupesh Gaire. "GENOTYPIC AND PHENOTYPIC CHARACTERIZATION OF PURDUE SOFT RED WINTER WHEAT BREEDING POPULATION." Thesis, 2020.
Знайти повний текст джерела(9410594), Ana Gabriela Morales Ona, James Camberato (9410608), and Robert Nielsen (9410614). "Using UAV-Based Crop Reflectance Data to Characterize and Quantify Phenotypic Responses of Maize to Experimental Treatments in Field-Scale Research." Thesis, 2020.
Знайти повний текст джерелаUnmanned 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.
Петренко, Олександра Олександрівна, та Oleksandra Oleksandrivna Petrenko. "Фенотипічний поліморфізм за рисунком пронотума та елітер імаго Leptinotarsa decemlineata Say на різних пасльонових культурах в умовах села Запсілля Краснопільського району Сумської області". Master's thesis, 2020. http://repository.sspu.edu.ua/handle/123456789/9660.
Повний текст джерелаThe 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.
(9224231), Dongdong Ma. "Ameliorating Environmental Effects on Hyperspectral Images for Improved Phenotyping in Greenhouse and Field Conditions." Thesis, 2020.
Знайти повний текст джерела(10233353), Behrokh Nazeri. "Evaluation of Multi-Platform LiDAR-Based Leaf Area Index Estimates Over Row Crops." Thesis, 2021.
Знайти повний текст джерелаHerden, Tobias. "Genetic analysis of Helosciadium repens (Jacq.) W.D.J.Koch populations in Germany - Fundamental research for conservation management." Doctoral thesis, 2020. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-202002032594.
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