A Low-cost Depth Imaging Mobile Platform for Canola Phenotyping

To meet the high demand for supporting and accelerating progress in the breeding of novel traits, plant scientists and breeders have to measure a large number of plants and their characteristics accurately. A variety of imaging methodologies are being deployed to acquire data for quantitative studies of complex traits. When applied to a large number of plants such as canola plants, however, a complete three-dimensional (3D) model is time-consuming and expensive for high-throughput phenotyping with an enormous amount of data. In some contexts, a full rebuild of entire plants may not be necessary. In recent years, many 3D plan phenotyping techniques with high cost and large-scale facilities have been introduced to extract plant phenotypic traits, but these applications may be affected by limited research budgets and cross environments. This thesis proposed a low-cost depth and high-throughput phenotyping mobile platform to measure canola plant traits in cross environments. Methods included detecting and counting canola branches and seedpods, monitoring canola growth stages, and fusing color images to improve images resolution and achieve higher accuracy. Canola plant traits were examined in both controlled environment and field scenarios. These methodologies were enhanced by different imaging techniques. Results revealed that this phenotyping mobile platform can be used to investigate canola plant traits in cross environments with high accuracy. The results also show that algorithms for counting canola branches and seedpods enable crop researchers to analyze the relationship between canola genotypes and phenotypes and estimate crop yields. In addition to counting algorithms, fusing techniques can be helpful for plant breeders with more comfortable access plant characteristics by improving the definition and resolution of color images. These findings add value to the automation, low-cost depth and high-throughput phenotyping for canola plants. These findings also contribute a novel multi-focus image fusion that exhibits a competitive performance with outperforms some other state-of-the-art methods based on the visual saliency maps and gradient domain fast guided filter. This proposed platform and counting algorithms can be applied to not only canola plants but also other closely related species. The proposed fusing technique can be extended to other fields, such as remote sensing and medical image fusion

Quantifying the effect of aerial imagery resolution in automated hydromorphological river characterisation

Existing regulatory frameworks aiming to improve the quality of rivers place hydromorphology as a key factor in the assessment of hydrology, morphology and river continuity. The majority of available methods for hydromorphological characterisation rely on the identification of homogeneous areas (i.e., features) of flow, vegetation and substrate. For that purpose, aerial imagery is used to identify existing features through either visual observation or automated classification techniques. There is evidence to believe that the success in feature identification relies on the resolution of the imagery used. However, little effort has yet been made to quantify the uncertainty in feature identification associated with the resolution of the aerial imagery. This paper contributes to address this gap in knowledge by contrasting results in automated hydromorphological feature identification from unmanned aerial vehicles (UAV) aerial imagery captured at three resolutions (2.5 cm, 5 cm and 10 cm) along a 1.4 km river reach. The results show that resolution plays a key role in the accuracy and variety of features identified, with larger identification errors observed for riffles and side bars. This in turn has an impact on the ecological characterisation of the river reach. The research shows that UAV technology could be essential for unbiased hydromorphological assessment

Preliminary assessment of industrial needs for an advanced ocean technology

A quick-look review of selected ocean industries is presented for the purpose of providing NASA OSTA with an assessment of technology needs and market potential. The size and growth potential, needs and problem areas, technology presently used and its suppliers, are given for industries involved in deep ocean mining, petrochemicals ocean energy conversion. Supporting services such as ocean bottom surveying; underwater transportation, data collection, and work systems; and inspection and diving services are included. Examples of key problem areas that are amenable to advanced technology solutions are included. Major companies are listed

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

Carbon exchange in boreal ecosystems: upscaling and the impacts of natural disturbances

Boreal forests and peatlands are globally significant carbon stores but they are threatened by rising air temperatures and changes in precipitation regimes and the frequency of natural disturbances. Predicting how the boreal biome will respond to climate change depends on being able to accurately model and upscale the greenhouse gas fluxes between these ecosystems and the atmosphere. This thesis focuses on developing simple, empirical, remote sensing models of ecosystem respiration (ER) and assessing how ER varies over space and in response to natural disturbances such as drought and wildfire. Paper I and II tested the opportunities and limitations offered by newly available, miniaturized thermal cameras, which can capture images of surface temperature at sub-decimeter resolution. Since temperature is one of the most important factors driving ER, these cameras provide an opportunity to map ER in unprecedented detail. In Paper III, satellite land surface temperature (LST) data was used to model ER across several Nordic peatland sites to examine whether remote sensing models can capture variations in ER between sites. In addition, Paper II and III highlighted the impacts of the extreme 2018 drought that affected large parts of Europe on peatland CO2 fluxes. The drought also led to a severe wildfire season in Sweden and Paper IV investigated how the effects of fire on forest soils depended on burn severity, salvage-logging and stand age.Producing reliable surface temperature measurements from miniaturized thermal cameras requires careful data collection and processing and one of the main outcomes of this thesis is a set of best practices for thermal camera users. Despite including larger uncertainties than traditional soil or air temperature measurements, thermal data from these cameras is suitable for modeling ER in peatland ecosystems. The ER maps that can be produced using UAV thermal cameras offer a unique resource for evaluating how ER varies within a flux tower footprint and could reveal potential biases in flux tower measurements. Indeed, this thesis demonstrated that there is substantial spatial variation in ER, both within and between peatlands. Vegetation composition played a significant role in driving this spatial variation as well as the response of peatlands to drought. Nevertheless, using only LST and the Enhanced Vegetation Index (EVI2) as model inputs captured a large proportion of the variability of daily ER across multiple peatlands. With further developments, such a modeling approach could represent a simple and effective way of estimating peatland ER across Scandinavia. In terms of wildfire impacts on boreal forest soils, stand age had a significant impact on soil respiration, nutrient availability and microclimate, whereas salvage-logging did not, in the first year after fire. Furthermore, the effects of drought and wildfire on ER depended on their severity, but during both extreme water stress in peatlands and after severe burning in forests, ER decreased. It is important that this negative feedback is accounted for in ER models to avoid overestimating carbon loss from northern ecosystems in response to disturbances and climate change