Real-Time Automatic Linear Feature Detection in Images

Linear feature detection in digital images is an important low-level operation in computer vision that has many applications. In remote sensing tasks, it can be used to extract roads, railroads, and rivers from satellite or low-resolution aerial images, which can be used for the capture or update of data for geographic information and navigation systems. In addition, it is useful in medical imaging for the extraction of blood vessels from an X-ray angiography or the bones in the skull from a CT or MR image. It also can be applied in horticulture for underground plant root detection in minirhizotron images. In this dissertation, a fast and automatic algorithm for linear feature extraction from images is presented. Under the assumption that linear feature is a sequence of contiguous pixels where the image intensity is locally maximal in the direction of the gradient, linear features are extracted as non-overlapping connected line segments consisting of these contiguous pixels. To perform this task, point process is used to model line segments network in images. Specific properties of line segments in an image are described by an intensity energy model. Aligned segments are favored while superposition is penalized. These constraints are enforced by an interaction energy model. Linear features are extracted from the line segments network by minimizing a modified Candy model energy function using a greedy algorithm whose parameters are determined in a data-driven manner. Experimental results from a collection of different types of linear features (underground plant roots, blood vessels and urban roads) in images demonstrate the effectiveness of the approach

Detecting Piecewise Linear Networks Using Reversible Jump Markov Chain Monte Carlo

This work proposes a piecewise linear network model to approximate structures observed in an image. An energy function is used to capture the characteristics of the structure. The energy function consists of two parts: the prior energy term and the data energy term. The prior energy term is calculated using prior information about the structures of interest. The data energy term is calculated using observations made from the image. The energy function is minimized using Reversible Jump Markov Chain Monte Carlo (RJMCMC) to get the approximate centerline of the structure. The algorithm was tested on a database of $150$ images containing underground roots taken by a minirhizotron camera. The results show the importance of a novel non-Gaussian term introduced to handle roots with low intensity near the centerline. It is possible to use the proposed model to detect other structures such as roads as shown by the preliminary results

Modelling Plant Variety Dependent Least Limiting Water Range (LLWR)

Drought stress is a major limiting factor for yield on a global scale (Solh and van Ginkel, 2014), with drought effects being predicted to become more severe with increasing global temperatures (IPCC, 2014). Climate change is also expected to increase the frequency and severity of floods leading to root oxygen stress (Trenberth, 2011). At the same time, current agricultural practises are increasingly relying on heavy machinery leading to soil compaction and changes in soil structure (Chamen et al., 2003), reducing the rate of cell division in the root meristem, and decreasing cell expansion (Bengough and Mullins, 1990). As such, in order to reduce yield losses it is essential to understand the complex interaction between oxygen stress, water stress and mechanical stress (Mohammadi et al., 2010). The least limiting water range (LLWR) is one such model which relates the above-mentioned soil stressors in order to estimate the soil moisture range in a particular soil for which plants should be less limited in terms of growth. However, the extent to which the LLWR considers the influence of root traits in changing its boundaries is currently limited. In order to be able to assess the effects of root trait variability on the LLWR boundaries while manipulating the LLWR soil stressors a minirhizotron based system (RS) was developed. This cheap (~£10 per unit), acrylic based, A3 sized system enabled in situ imaging of roots and root hairs. Destructive sampling methods were also used to determine root border cell numbers and root tip geometry. To further optimise the process of data collection, Rcpp based image processing algorithms were developed to obtain automated estimates of the root traits of root length, root hair, root border cells and root tip eccentricity to further increase the efficiency of the RS phenotyping platform. To test how contrasting root traits influence the LLWR a plant phenotyping experiment was performed comparing four spring barley (Hordeum vulgare L.) varieties, Optic, KWS Sassy, Derkado and Golden Promise. Root growth rates both in the vertical and horizontal directions all increased with increasing water availability and decreasing substrate density. Root hair area did not vary significantly among treatments and between variaties. Root border cell count and root tip eccentricity increased with increasing substrate density but did not vary significantly across varieties. A root micro-trait based linear interaction model was developed to describe average root growth rates and it was demonstrated that root growth rates on average follow a linear patern for values >= 8 mm day-1. Root micro-traits mostly failed to correlate well with root growth rates except for a negative assosiation with root tip geometry (cor = -0.4192, p = 2e-05**)

Modelling Plant Variety Dependent Least Limiting Water Range (LLWR)

Drought stress is a major limiting factor for yield on a global scale (Solh and van Ginkel, 2014), with drought effects being predicted to become more severe with increasing global temperatures (IPCC, 2014). Climate change is also expected to increase the frequency and severity of floods leading to root oxygen stress (Trenberth, 2011). At the same time, current agricultural practises are increasingly relying on heavy machinery leading to soil compaction and changes in soil structure (Chamen et al., 2003), reducing the rate of cell division in the root meristem, and decreasing cell expansion (Bengough and Mullins, 1990). As such, in order to reduce yield losses it is essential to understand the complex interaction between oxygen stress, water stress and mechanical stress (Mohammadi et al., 2010). The least limiting water range (LLWR) is one such model which relates the above-mentioned soil stressors in order to estimate the soil moisture range in a particular soil for which plants should be less limited in terms of growth. However, the extent to which the LLWR considers the influence of root traits in changing its boundaries is currently limited. In order to be able to assess the effects of root trait variability on the LLWR boundaries while manipulating the LLWR soil stressors a minirhizotron based system (RS) was developed. This cheap (~£10 per unit), acrylic based, A3 sized system enabled in situ imaging of roots and root hairs. Destructive sampling methods were also used to determine root border cell numbers and root tip geometry. To further optimise the process of data collection, Rcpp based image processing algorithms were developed to obtain automated estimates of the root traits of root length, root hair, root border cells and root tip eccentricity to further increase the efficiency of the RS phenotyping platform. To test how contrasting root traits influence the LLWR a plant phenotyping experiment was performed comparing four spring barley (Hordeum vulgare L.) varieties, Optic, KWS Sassy, Derkado and Golden Promise. Root growth rates both in the vertical and horizontal directions all increased with increasing water availability and decreasing substrate density. Root hair area did not vary significantly among treatments and between variaties. Root border cell count and root tip eccentricity increased with increasing substrate density but did not vary significantly across varieties. A root micro-trait based linear interaction model was developed to describe average root growth rates and it was demonstrated that root growth rates on average follow a linear patern for values >= 8 mm day-1. Root micro-traits mostly failed to correlate well with root growth rates except for a negative assosiation with root tip geometry (cor = -0.4192, p = 2e-05**)

Extracting root system architecture from X-ray micro computed tomography images using visual tracking

X-ray micro computed tomography (µCT) is increasingly applied in plant biology as an imaging system that is valuable for the study of root development in soil, since it allows the three-dimensional and non-destructive visualisation of plant root systems. Variations in the X-ray attenuation values of root material and the overlap in measured intensity values between roots and soil caused by water and organic matter represent major challenges to the extraction of root system architecture. We propose a novel technique to recover root system information from X-ray CT data, using a strategy based on a visual tracking framework embedding a modiffed level set method that is evolved using the Jensen-Shannon divergence. The model-guided search arising from the visual tracking approach makes the method less sensitive to the natural ambiguity of X-ray attenuation values in the image data and thus allows a better extraction of the root system. The method is extended by mechanisms that account for plagiatropic response in roots as well as collision between root objects originating from different plants that are grown and interact within the same soil environment. Experimental results on monocot and dicot plants, grown in different soil textural types, show the ability of successfully extracting root system information. Various global root system traits are measured from the extracted data and compared to results obtained with alternative methods

Keskkonnamuutuste mõju hemiboreaalsete puuliikide ökofüsioloogiale – maa-alused kohanemismehhanismid

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKäesoleva sajandi lõpuks prognoositakse põhjapoolsetele laiuskraadidele koos temperatuuri ja sademete hulga kasvuga ka õhuniiskuse tõusu. Puude kasvukiirus võib suurenenud õhuniiskusel olla pärsitud ja transpiratsioonist tingitud veevoog läbi puude aeglustub, mis mõjutab vee ja vees lahustunud toitainete liikumist mullas. Kõige vähem on meil teadmisi, kuidas mõjutavad keskkonnamuutused taimede juuri ning vee- ja aineringeid mullas. Puude peened juured, mille ülesandeks on vee ja toitainete omastamine mullast, kohanevad keskkonnamuutustega. Näiteks muutub uute moodustuvate juurte kuju ja suurus ning kui selle tagajärjel suureneb juurte imav pind, siis toitainete kättesaamine paraneb. Lisaks sellele eritavad juured mulda mitmesuguseid orgaanilisi ühendeid. Juureeritised on toiduks juurte pinnal ja lähiümbruses elavatele mikroobidele, kes muudavad mineraalained mullas taimede jaoks kättesaadavamaks. Juureeritistega mulda viidavat süsinikuvoogu on keeruline mõõta ja veel vähem teatakse, kuidas mõjutavad seda keskkonnamuutused. Antud doktoritöö raames analüüsiti viie puuliigi ökofüsioloogilist kohanemist kõrgenenud õhuniiskusele erineva lämmastikuallikaga muldadel. Töö käigus lahendati ka palju metoodilisi küsimusi, näiteks kui palju mõjutab juureeritiste kogust imi-, kasvu- ja juhtejuurte osakaal peenjuurestikus või kuidas analüüsida juurte kasvudünaamikat ajas ning kas kõrgenev õhuniiskus või erinev lämmastikuallikas mõjutavad juurte kasvukäiku oluliselt? Selleks kasutati mitmeid nutikaid lahendusi, näiteks mobiiltelefoni, et pildistada juurte kasvu ning masinõpet piltide analüüsimiseks. Tulemused on uudsed: 1) juureeritiste voog võib olla märkimisväärne ning seostub taime kasvu ja talitlusega, 2) juureeritiste mõõtmisel tuleb arvestada juurestiku talituslikku koostist ja osa puuliike ei ole tundlikud õhuniiskuse muutumisele, 3) õhuniiskuse suurenemine muudab juurte arengut oluliselt, kiireneb vananemine ning väheneb imav pindala, 4) nutitelefonide ja masinõppe kombinatsioon on lihtne, kiire ja täpne meetod juurte kasvu mõõtmiseks laborkatsetes.By the end of this century, the predicted increase in air temperature and precipitation in Northern latitudes will lead to an increase in relative air humidity. Previous studies have shown a decrease in the growth rate of trees. Higher humidity decreases the transpiration rate, which diminishes the force facilitating water and nutrients towards roots. We have limited information on how these changes affect plant roots and water or nutrient cycles in soil. When nutrient availability in soil decreases, trees’ fine roots change their shape and form of new roots, and with the increasing absorptive area, the uptake of nutrients is enhanced. In addition, roots can exude different organic compounds into the soil, which is a food source for soil microbes. These microbes on roots and in the rhizosphere can meediate the necessary mineral nutrient uptake for plants. Measuring these root exudates is difficult, and the effects of environmental changes are unknown. In this study, we analysed the acclimation mechanisms of five tree species to high humidity and different nitrogen forms in soil. We measured how much fine root carbon exudation depends on the functional share of absorptive, pioneer and transport roots, which are all fine roots. Additionally, we implemented smart technologies to measure fine root growth dynamics by taking pictures with mobile phones and analysing the images with machine-learning-based software. Novel outcomes from this study include: 1) fine roots exude substantial amounts of carbon into soil, which is related to plant physiological parameters; 2) future studies measuring fine root exudates should consider root functional distribution and growth of some tree species was less affected by increased air humidity; 3) constant high humidity can change the development of fine roots, intensify root senescence and increase the absorptive area in fine roots; 4) the combination of smartphones and deep-learning methods is easy, fast and accurate to measure fine root growth in growth chambers

Efecto de un humato de calcio y fulvato de hierro en la calidad y producción de chile jalapeño (capsicum annum l.) y piquín (capsicum annum l. aviculare) y en la porosidad en un suelo calcisol

Los extractos orgánicos en la actualidad tienen gran aceptación por los productores agrícolas, fundamentalmente por su costo y el éxito de sus aplicaciones en los cultivos. Con el fin de obtener un compuesto orgánico-mineral o humato como alternativa para eficientar el uso de los nutrientes a los cultivos, se recolectó un mineral fósil orgánico de una mina, en laboratorio se extrajeron las fracciones de ácido húmico (AH) y fúlvico (AF), se ajustó el pH de estos ácidos con fertilizantes químicos. Para los ácidos húmicos y fúlvicos se manejó un pH de 6, 7, 8 y 4, 6, 7 respectivamente y finalmente se solidificaron para la caracterización de los grupos funcionales de estos ácidos; se utilizó un espectrómetro de luz infrarroja (IR). Los resultados muestran que los ácidos húmicos y fúlvicos presentan grupos funcionales similares pero en diferente cantidad, las mezclas orgánico-minerales de AH y AF mostraron un comportamiento similar en adsorción del elemento incorporado, sin embargo el AH y AF ambos con pH 7 presentaron la mayor adsorción del Ca y Fe, esto se debe a que son compuestos pH dependientes y la formación de agrupamientos se debe a las reacciones de intercambio catiónico dada por los radicales libres