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Computer vision methods for the analysis of multimodal and multidimensional data for high-throughput plant phenotyping
No full textDrought and nutrient stresses substantially impact crop productivity, frequently resulting
in lower yields and financial losses to farmers. Early detection and tracking of these
stresses are significant for improving agricultural yield and achieving the world food
challenge goals. Advanced image technologies such as the use of conventional cameras
and imaging spectroscopy combined with computer vision methodolgoies are being
leveraged for non-invasive plant phenotyping to improve crop productivity, resilience,
and sustainability. This thesis seeks to combine these technologies with machine learning
algorithms to develop automated phenotyping pipelines to phenotype plants at different
growth stages, particularly; for nutrient and drought stress identification and
quantification. With this proposal, the onset of plant nutrient and drought stresses could
be detected early and at different growth stages of the plants. Three experiments (two in
the glasshouse and one in the field) were conducted, and images were acquired using
digital RGB and a hyperspectral camera. The first experiment (nutrient stress) was
performed in the glasshouse, the second (nutrient stress) on the field and the third (drought
stress) in the glasshouse. The glasshouse nutrient experiment was performed on quinoa
and cowpea plants made of four treatments: high nitrogen high phosphorus (HNHP), high
nitrogen low phosphorus (HNLP), low nitrogen low phosphorus (LNHP) and low
nitrogen low phosphorus (LNLP). The field nutrient experiment was performed on wheat
plants made of 12 Olsen phosphorus varitions (approximately 3, 6, 9, 12, 15, 18, 21, 25,
30, 40, 50 and 60 ppm). The third experiment is a wheat drought analysis under variable
nitrogen at selected plant growth stages. The treatments include: well-watered high-
nitrogen (WWHN), well-watered low-nitrogen (WWLN), drought-stress high-nitrogen
(DSHN) and drought-stress low-nitrogen (DSLN). Several image processing and machine
learning techniques were employed to pre-process, post-process, and analyze plant-
specific traits for tracking plant drought and nutrient stresses. Specifically, using digital
imaging, a new segmentation algorithm invariant to illumination and complex
background scenes was proposed to segment field and glasshouse-based images.
Statistical and machine learning methods were employed to identify phenotypic traits
sensitive to nutrient (nitrogen and phosphorus) deficiencies. Additionally, plant colour,
morphology, and texture features were critically analyzed to assess their response to
different stresses in plants. Using a hyperspectral imaging, a hybrid deep learning model
was proposed to detect and track plant nitrogen and phosphorus deficiencies. In this case
, the spatial and spectral characteristics of plants were analyzed, and deep learning
algorithms were combined to understand their response to nutrient and drought stress in
plants. Finally, using the spectral characteristics of plants, different conventional machine
learning algorithms including Random Forest, Partial Least Square Regression and
Support Vector Machines were developed to model the trends and patterns of plants for
drought stress detection. The research results show a link between colour and nutrient
stress, while texture and colour features were highly responsive to drought stress. The
short-wave infrared region of the electromagnetic spectrum was highly responsive to
plant phosphorus deficiency, while the blue, red, and near-infrared regions were highly
connected to plant nitrogen deficiency. Furthermore, combining a proposed vegetation
indices (VIs) from the VNIR regions of the spectrum with already known VIs resulted in
the easy identification of plant drought stress compared to using only the known or
proposed indices.PhD in Environment and Agrifoo
Digital transformation and profit growth: a configurational analysis of regional dynamics
Get PDFThis study adopts Configuration Theory to explore how diverse combinations of regional factors contribute to profitability, emphasizing the principle of equifinality, which posits that multiple, equally effective configurations can lead to similar outcomes. This study examines the interplay of multiple factors—enterprise informatization, digital infrastructure, e-commerce, technological investment, innovation, hardware, and software—across four key themes: Digital Readiness and Technological Integration, Market and Economic Enablers, Innovation Capacity and Activity, and Foundational Artifacts and Resources. Using data from 31 provinces in China from 2015 to 2022, this study employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to uncover pathways to regional profit growth. The study identifies five distinct configurations contributing to profit growth across China's provinces. In most configurations, e-commerce and technological investment emerge as central drivers. However, in less developed regions, profit growth relies more on improvements in digital infrastructure and hardware, with innovation and enterprise informatization playing a less significant role. The findings also reveal that profit growth requires addressing the weakest elements in the ecosystem—whether digital infrastructure, technological capabilities, or other factors. Strategies tailored to regional conditions must prioritize improving these weaker components to achieve sustained growth, as ignoring them can limit overall success.IEEE Transactions on Engineering Managemen
Chip away everything that doesn't look like an elephant
Get PDFThis paper addresses the question of how conceptual models are created in a simulation modelling activity. Assuming an entity-based approach to simulation, some techniques for discovering good entity classes are considered, including personation. Also considered are the notations by which a conceptual model can be represented, and the modes of thought required for good conceptual modelling. Specifically excluded from consideration is the idea of applying a cut-and-dried method. The shortcomings of computers for conceptual modelling are remarked upon.12th Simulation Workshop (SW25
Categorizing errors in high‐reliability organizations: adaptive range and adaptive capacity in incident response
Get PDFThis study examines how actors in a high‐reliability organization categorize errors as near‐misses or accidents through the lens of adaptive capacity and adaptive range. We studied a large defense entity with operations critical to national security to understand how organization members categorized errors during incidents. Using the repertory grid method to interview informants, we identify key dualities that actors navigate between anticipatory and retrospective responses to errors. These dualities collectively reflect the organization's adaptive capacity and adaptive range when balancing anticipatory and retrospective responses. Our analysis of error categorization through this lens provides new insights into how high‐reliability organizations manage incidents to maintain reliability and offers practical implications for enhancing organizational resilience in high‐risk settings.Risk Analysi
Analyzing frictional noise for wear monitoring under dry and lubrication condition: experimental modelling with pin-on-disc tribometer
Get PDFIn industrial settings, the use of frictional noise to improve wear monitoring is highly promising. It enables the identification of changes in friction and wear conditions, the assessment of different phases of wear, and the examination of the impact of wear on machine performance. By analysing acoustic signatures, it is conceivable to continuously monitor the wear characteristics and surface conditions. This helps in predicting wear and detecting aberrant wear regimes in real-time. The data demonstrate that in dry conditions, the aluminum disc has higher coefficients of friction relative to cast iron and mild steel, likely due to the absence of graphite flakes in aluminum. Under lubricated conditions, a layer of lube significantly decreases the coefficient of friction, with no apparent deviations across the materials, demonstrating that complete lubrication avoids direct metal contact. In lubrication-starved applications, oily depictions nevertheless help minimize friction, though less efficiently than complete lubrication. In dry conditions, frictional sound levels for mild steel are higher due to direct surface hits, while lubrication reduces noise by eliminating metal-on-metal contact. As a result, monitoring noise levels is a helpful indicator of lubrication difficulties, aiding in maintenance and repairs.The 12th International Conference on Fracture Fatigue and WearAdvances in Science and Technolog
Quantifying the carbon footprint of events: a life cycle assessment-based framework for evaluating impact of location and timing
Get PDFThis research proposes a Life Cycle Assessment-based framework to quantify the carbon footprint of events, considering the event's location and timing. The framework aims to standardise environmental impact calculations through inventory analysis. To validate it, a comparative analysis on conducting an event in different locations and time periods, while maintaining similar scale and nature is conducted. The assessment includes emissions from attendee transport, accommodation, food and drink, and venue. Additionally, it considers emission reductions resulting from attendees not using their personal household resources. This accounts for the actual additional emissions released into the atmosphere as a consequence of the event. The results highlight variations in emissions across different consumption categories based on the selected location and timing. By providing this information, the LCA-based framework provides valuable guidance for event organizers and policymakers to assess event environmental impacts and promote sustainability.19th Conference on Sustainable ManufacturingSustainable Manufacturing as a Driver for Growth (GCSM 2023
Board and executive gender diversity as a driver of airline efficiency: a network-DEA analysis
Get PDFThis paper investigates the influence of gender diversity on the efficiency of airlines. Despite global progress towards gender equality, the airline industry continues to exhibit a considerable gender gap, especially in leadership positions. Our study utilises a Network Data Envelopment Analysis (DEA) to assess the performance of airlines concerning gender diversity in board and executive roles. By analysing a dataset of airlines of different continents and covering the period before and after the COVID-19 crisis, gender diversity's impact on airline efficiency (defined as the ratio of the sum of its weighted outputs to the sum of its weighted inputs) is examined. A second-stage estimation further enriches the analysis to assess the influence of external variables. Our findings reveal that airlines with higher gender diversity on their boards and executive teams exhibit better efficiency before and after COVID-19 crisis. The study contributes to the broader discourse on gender diversity in traditionally male-dominated sectors, offering insights into the strategic benefits of inclusive leadership practices.Journal of Air Transport Managemen
Development of sustainable PPE for higher threat clearance operations by humanitarian organisations
Get PDFIn 2020 it was reported that 59 countries and territories were still contaminated by both landmines and other unexploded ordnance. The threats faced by those in the demining industry have increased due to the more frequent use of IEDs. These will generally have a larger charge size than conventional mines. PPE therefore needs to be improved to keep in line with these new threats. Recently there has been a push from industry to include more sustainable materials within armour production, with some already showing some promise within literature. Ramie (Boehmeria nivea) fibres have also been previously used in studies surrounding in soft armour, hard armour, and multi-layered armour system (MAS) and Flax (Linum usitatissimum) has been investigated with regards to hard armour panels. The hard panels manufacture for this project were produced using the vacuum resin infusion method, a low-cost method that is easy enough to be used in low scale operations. A number of different configurations were created, including natural fibre para-aramid and all natural fibre combinations, all of which were subject to different mechanical tests to assess their strengths. The ballistic testing, completed on a single stage light gas gun, showed the 50% Ramie 50% para-aramid panels outperformed all others tested. A significant observation was the different
fracture mechanics seen on the back face of each of the panels, something that may have contributed towards their good performance. The all-natural Flax Ramie panels also showed some significant results, with these panels outperforming several other panels which contained para-aramid materials. Future work in this project will include assessing the best performing panels under increased ballistic loads, as well as looking at the suitability aspect of these panels to determine whether they can be potentially recycled once they have been used.Engineering and Physical Sciences Research Council (EPSRC)DNV Energy Systems, UKDefence and Security Doctoral Symposia 2024 (DSDS24
Comparative sampling methodologies for detecting and quantifying 2,4,6 trinitrotoluene post-blast traces in water
Get PDFThis work is a preliminary study to provide an overview of grab, composite and multi-increment sampling.This study addresses the analytical challenges associated with recovering explosive residues, focusing on the identification of 2,4,6-trinitrotoluene (TNT) in water samples. It evaluates the practicality, efficiency, and representativeness of three sampling methodologies: traditional grab sampling (GS), composite sampling (CS), and 3-D multi-increment sampling (3D-MIS). High-Performance Liquid Chromatography (HPLC) was employed for explosive identification. Post-blast sampling of TNT residues from high-order and low-order deflagrations was conducted to assess each method's efficacy and limitations in detecting trace and bulk contaminations. The experiments were conducted at the Alford Technologies Group range in Broadmead, UK, with analysis performed at the Defence Academy in Shrivenham, UK. Key findings highlight the varying effectiveness of each sampling method, with implications for enhancing detection sensitivity and accuracy in post-blast scenarios. This study underscores the importance of selecting appropriate sampling strategies tailored to different contamination scenarios, thereby informing more effective response protocols in CBRNE incidents involving water environments.Work was carried out at Alford Technologies Range.Defence and Security Doctoral Symposia 2024 (DSDS24
Environmental impact assessment of manufacturing of SiC/SiC composites
Get PDFSiC/SiC composites have attracted increasing attention in various applications such as turbine blades, exhaust nozzles, and combustor chambers, due to their exceptional mechanical and thermal properties. However, the environmental impact of these composites across their life cycle is an important aspect that needs to be evaluated to support their responsible development and use. In this study, a life cycle assessment of SiC/SiC woven laminate ceramic matrix composites to quantify their environmental impacts from cradle-to-gate was conducted. Three different manufacturing methods to produce SiC/SiC woven laminates were researched: chemical vapour infiltration (CVI), pyrolysis of a preceramic polymer (PIP), and melt infiltration (MI). The Life Cycle Assessment approach was utilized to identify the effect outcomes for each process, analysing the raw material extraction, raw material processing, and final product manufacturing phases to develop the environmental impact assessment. The study's outcome showed that CVI had the lowest average environmental impact between the two methods.This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 886840.19th Conference on Sustainable ManufacturingSustainable Manufacturing as a Driver for Growth (GCSM 2023