Stanley J. Miklavcic

A Riemannian Elastic Metric for Shape-Based Plant Leaf Classification

The shapes of plant leaves are of great importance to plant biologists and botanists, as they can help to distinguish plant species and measure their health. In this paper, we study the performance of the Squared Root Velocity Function (SRVF) representation of closed planar curves in the analysis of plant-leaf shapes. We show that it provides a joint framework for computing geodesics (registration) and similarities between plant leaves, which we use for their automatic classification. We evaluate its performance using standard databases and show that it outperforms significantly the state-of-the-art descriptor-based techniques. Additionally, we show that it enables the computation of shape statistics, such as the average shape of a leaf population and its principal directions of variation, suggesting that the representation is suitable for building generative models of plant- leaf shapes.

Landmark-free statistical analysis of the shape of plant leaves

The shapes of plant leaves are important features to biologists, as they can help in distinguishing plant species, measuring their health, analyzing their growth patterns, and understanding relations between various species. Most of the methods that have been developed in the past focus on comparing the shape of individual leaves using either descriptors or finite sets of landmarks. However, descriptor-based representations are not invertible and thus it is often hard to map descriptor variability into shape variability. On the other hand, landmark-based techniques require automatic detection and registration of the landmarks, which is very challenging in the case of plant leaves that exhibit high variability within and across species. In this paper, we propose a statistical model based on the Squared Root Velocity Function (SRVF) representation and the Riemannian elastic metric of Srivastava et al. (2011) to model the observed continuous variability in the shape of plant leaves. We treat plant species as random variables on a non-linear shape manifold and thus statistical summaries, such as means and covariances, can be computed. One can then study the principal modes of variations and characterize the observed shapes using probability density models, such as Gaussians or Mixture of Gaussians. We demonstrate the usage of such statistical model for (1) efficient classification of individual leaves, (2) the exploration of the space of plant leaf shapes, which is important in the study of population-specific variations, and (3) comparing entire plant species, which is fundamental to the study of evolutionary relationships in plants. Our approach does not require descriptors or landmarks but automatically solves for the optimal registration that aligns a pair of shapes. We evaluate the performance of the proposed framework on publicly available benchmarks such as the Flavia, the Swedish, and the ImageCLEF2011 plant leaf datasets.

RootGraph: a graphic optimization tool for automated image analysis of plant roots

Highlight The method presented analyses root scans automatically, distinguishes primary from lateral roots, and quantifies a broad range of traits for individual primary roots and their associated lateral roots.

The effect of surface and hydrodynamic forces on the shape of a fluid drop approaching a solid surface

This paper describes an experiment designed to measure surface and hydrodynamic forces between a mercury drop and a flat mica surface immersed in an aqueous medium. An optical interference technique allows measurement of the shape of the mercury drop as well as its distance from the mica, for various conditions of applied potential, applied pressure, and solution conditions. This enables a detailed exploration of the surface forces, particularly double-layer forces, between mercury and mica. A theoretical analysis of drop shape under the influence of surface forces shows that deformation of the drop is a sensitive indicator of the forces, as well as being a very important factor in establishing the overall interaction between the solid and the fluid.

Root phenotyping by root tip detection and classification through statistical learning

AimsRoot branching is a fundamental phenotypic property of a root system. In this paper we present a system for the fully automated detection and classification of root tips in root images obtained either by 2D flat bed scanning or by 3D digital camera imaging. With our system we aim to provide a robust, efficient and accurate means of phenotyping of roots.MethodsStructural information derived from image features such as root ends and root branches is utilised for the detection and classification processes. A statistical analysis based on training data sets of root tips and non-root tips is used to assign image features to one of three different classes: non-root tips, primary root tips and lateral root tips. The automated procedure is optimised to ensure as high true detection rate and low false detection rate as possible.ResultsWe apply the method to images of barley, rice, and corn roots taken either by 2D scanning of washed and cut roots or digital camera images of plant roots growing in a transparent medium. The results of our detection and classification procedure are validated by a comparison with manually labelled images for all three species. Our results are also compared to two established platforms, EZ-Rhizo and WinRHIZO. Finally, we demonstrate the utility of the statistical learning approach by quantifying root phenotypic properties of barley double haploid lines.ConclusionsThe method of statistical learning of characteristic features is an accurate means of not only counting root numbers, but also discriminating between primary and lateral roots. The fully-automated procedure presented in this paper can be used reliably in high throughput situations to characterise quantitative phenotypic variation.

Dynamic Dielectric Response of Concentrated Colloidal Dispersions: Comparison between Theory and Experiment†

The cell-model electrokinetic theory of Ahualli et al. Langmuir 2006, 22, 7041; Ahualli et al. J. Colloid Interface Sci. 2007, 309, 342; and Bradshaw-Hajek et al. Langmuir 2008, 24, 4512 is applied to a dense suspension of charged spherical particles, to exhibit the systems dielectric response to an applied electric field as a function of solids volume fraction. The models predictions of effective permittivity and complex conductivity are favorably compared with published theoretical calculations and experimental measurements on dense colloidal systems. Physical factors governing the volume fraction dependence of the dielectric response are discussed.

Polyethyleneimine for copper absorption II: kinetics, selectivity and efficiency from seawater

Glutaraldehyde (GA) cross-linked polyethyleneimine (PEI) coatings have previously been reported to effectively and selectively take up copper from seawater relevant concentrations in artificial seawater. We evaluate the copper uptake of such coatings from natural seawater. X-ray photoelectron spectroscopy elemental analysis revealed the coatings to be highly efficient and equally selective for copper uptake in natural seawater, reaching a maximum copper loading of 2 wt% in 48 hours. Similar to observations in artificial seawater we found that zinc was initially accumulated in the coatings, but was exchanged by copper over time. We investigate the spatial distribution of copper in the coatings by time-of-flight secondary ion mass spectrometry (ToF-SIMS), which revealed that copper was evenly distributed in the coating, with the exception of lower concentrations at the coating-water interface. We use synchrotron X-ray absorption studies and Fourier transform infrared (FTIR) spectroscopy to show that the copper–ligand interaction was mediated by Schiffs bases (imines).

Modeling Root Zone Effects on Preferred Pathways for the Passive Transport of Ions and Water in Plant Roots

We extend a model of ion and water transport through a root to describe transport along and through a root exhibiting a complexity of differentiation zones. Attention is focused on convective and diffusive transport, both radially and longitudinally, through different root tissue types (radial differentiation) and root developmental zones (longitudinal differentiation). Model transport parameters are selected to mimic the relative abilities of the different tissues and developmental zones to transport water and ions. For each transport scenario in this extensive simulations study, we quantify the optimal 3D flow path taken by water and ions, in response to internal barriers such as the Casparian strip and suberin lamellae. We present and discuss both transient and steady state results of ion concentrations as well as ion and water fluxes. We find that the peak in passive uptake of ions and water occurs at the start of the differentiation zone. In addition, our results show that the level of transpiration has a significant impact on the distribution of ions within the root as well as the rate of ion and water uptake in the differentiation zone, while not impacting on transport in the elongation zone. From our model results we infer information about the active transport of ions in the different developmental zones. In particular, our results suggest that any uptake measured in the elongation zone under steady state conditions is likely to be due to active transport.

Frequency-dependent electrical conductivity of concentrated dispersions of spherical colloidal particles.

This paper outlines the application of a self-consistent cell-model theory of electrokinetics to the problem of determining the electrical conductivity of a dense suspension of spherical colloidal particles. Numerical solutions of the standard electrokinetic equations, subject to self-consistent boundary conditions, are implemented in formulas for the electrical conductivity appropriate to the particle-averaged cell model of the suspension. Results of calculations as a function of frequency, zeta potential, volume fraction, and electrolyte composition, are presented and discussed.

Mathematical modelling of the uptake and transport of salt in plant roots.

In this paper, we present and discuss a mathematical model of ion uptake and transport in roots of plants. The underlying physical model of transport is based on the mechanisms of forced diffusion and convection. The model can take account of local variations in effective ion and water permeabilities across the major tissue regions of plant roots, represented through a discretized coupled system of governing equations including mass balance, forced diffusion, convection and electric potential. We present simulation results of an exploration of the consequent enormous parameter space. Among our findings we identify the electric potential as a major factor affecting ion transport across, and accumulation in, root tissues. We also find that under conditions of a constant but realistic level of bulk soil salt concentration and plant-soil hydraulic pressure, diffusion plays a significant role even when convection by the water transpiration stream is operating.