Thesis (Ph.D.)--Aberdeen University, 2009. / Title from web page (viewed on July 8, 2009). Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 113-121). Also available in electronic version.

Techn. Universiẗat, Diss., 2002--Freiberg (Sachsen).

Universiẗat, Diss., 2004--Stuttgart.

Techn. Hochsch., Diss., 2005--Aachen.

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.

Mitochondria exist in a dynamic network whereby fusion and fission events are critical to the health of the mitochondria, the cell, and the organism. Dysfunctional mitochondrial dynamics underlie a plethora of diseases including various cancers, heart diseases, diabetes, neurodegenerative diseases, and a number of mitochondrial disorders. Despite a strong molecular knowledge of a handful of functional mediators of mitochondrial dynamics, much less is known about how this process is regulated at a cellular level, and what genes are involved in signaling pathways. A previously completed mitochondrial morphology genome screen was repeated with an automated confocal microscope resulting in the identification and validation of MTX2 as a novel regulator of mitochondrial dynamics. Functional characterization of the role of MTX2 in mitochondrial dynamics will further our understanding of mitochondrial biology, and has the future potential to inform therapies for some of the many diseases underscored by dysfunctional mitochondrial dynamics.

The thesis deals with data network testing. The aim of the thesis is to design a methodology for the comprehensive measurement of network transmission parameters and design of the tester concept and realisation by helping to extend the existing JMeter program.

The optimization of the performance of a constant Work-in-Progress (CONWIP) production system through the sequencing of its backlog list is investigated. The performance measures considered are throughput, optimum WIP level (m*), and flow time. Analysis of the effects of sequence dependent bottlenecks on system performance is provided. A procedure is presented to determine a lower bound for (m*) given the product mix. A method that determines (m*) given the sequence of jobs is provided. A heuristic algorithm is provided for the purpose of determining a sequence to minimize (m*). The algorithm attempts to sequence the jobs to achieve the "best fit" between consecutive jobs so that machine and job idle times are minimized. The algorithm is tested through computer implementation to reveal its proficiency. / Master of Science

With growing concerns about global food supply and environmental impacts of modern agriculture, we are seeing an increased demand for more horticultural research. While research into plant genetics has seen an increased throughput from recent technological advancements, plant phenotypic research throughput has lagged behind. Improvements in open-source image processing software and image capture hardware have created an opportunity for the development of more competitively-priced, faster data-acquisition tools. These tools could be used to collect measurements of plants' phenotype on a much larger scale without sacrificing data quality. This paper demonstrates the feasibility of creating such a tool. The resulting design utilized stereo vision and image processes in the OpenCV project to measure a representative collection of observable plant traits like leaflet length or plant height. After the stereo camera was assembled and calibrated, visual and stereo images of potato plant canopies and tubers(potatoes) were collected. By processing the visual data, the meaningful regions of the image (the canopy, the leaflets, and the tubers) were identified. The same regions in the stereo images were used to determine plant physical geometry, from which the desired plant measurements were extracted. Using this approach, the tool had an average accuracy of 0.15 inches with respect to distance measurements. Additionally, the tool detected vegetation, tubers, and leaves with average Dice indices of 0.98, 0.84, and 0.75 respectively. To compare the tool's utility to that of traditional implements, a study was conducted on a population of 27 potato plants belonging to 9 separate genotypes. Both newly developed and traditional measurement techniques were used to collect measurements of a variety of the plants' characteristics. A multiple linear regression of the plant characteristics on the plants' genetic data showed that the measurements collected by hand were generally better correlated with genetic characteristics than those collected using the developed tool; the average adjusted coefficient of determination for hand-measurements was 0.77, while that of the tool-measurements was 0.66. Though the aggregation of this platform's results is unsatisfactory, this work has demonstrated that such an alternative to traditional data-collection tools is certainly attainable. / Master of Science