Flowers, leaves or both? How to obtain suitable images for automated plant identification

Background: Deep learning algorithms for automated plant identification need large quantities of precisely labelled images in order to produce reliable classification results. Here, we explore what kind of perspectives and their combinations contain more characteristic information and therefore allow for higher identification accuracy. Results: We developed an image-capturing scheme to create observations of flowering plants. Each observation comprises five in-situ images of the same individual from predefined perspectives (entire plant, flower frontal- and lateral view, leaf top- and back side view). We collected a completely balanced dataset comprising 100 observations for each of 101 species with an emphasis on groups of conspecific and visually similar species including twelve Poaceae species. We used this dataset to train convolutional neural networks and determine the prediction accuracy for each single perspective and their combinations via score level fusion. Top-1 accuracies ranged between 77% (entire plant) and 97% (fusion of all perspectives) when averaged across species. Flower frontal view achieved the highest accuracy (88%). Fusing flower frontal, flower lateral and leaf top views yields the most reasonable compromise with respect to acquisition effort and accuracy (96%). The perspective achieving the highest accuracy was species dependent. Conclusions: We argue that image databases of herbaceous plants would benefit from multi organ observations, comprising at least the front and lateral perspective of flowers and the leaf top view

Independent Ion Migration in Suspensions of Strongly Interacting Charged Colloidal Spheres

We report on sytematic measurements of the low frequency conductivity in aequous supensions of highly charged colloidal spheres. System preparation in a closed tubing system results in precisely controlled number densities between 1E16/m3 and 1E19/m^3 (packing fractions between 1E-7 and 1E-2) and electrolyte concentrations between 1E-7 and 1E-3 mol/l. Due to long ranged Coulomb repulsion some of the systems show a pronounced fluid or crystalline order. Under deionized conditions we find s to depend linearily on the packing fraction with no detectable influence of the phase transitions. Further at constant packing fraction s increases sublinearily with increasing number of dissociable surface groups N. As a function of c the conductivity shows pronounced differences depending on the kind of electrolyte used. We propose a simple yet powerful model based on independent migration of all species present and additivity of the respective conductivity contributions. It takes account of small ion macro-ion interactions in terms of an effectivly transported charge. The model successfully describes our qualitatively complex experimental observations. It further facilitates quantitative estimates of conductivity over a wide range of particle and experimental parameters.Comment: 32 pages, 17 figures, 2 tables, Accepted by Physical Review

Influence of solvent granularity on the effective interaction between charged colloidal suspensions

We study the effect of solvent granularity on the effective force between two charged colloidal particles by computer simulations of the primitive model of strongly asymmetric electrolytes with an explicitly added hard sphere solvent. Apart from molecular oscillating forces for nearly touching colloids which arise from solvent and counterion layering, the counterions are attracted towards the colloidal surfaces by solvent depletion providing a simple statistical description of hydration. This, in turn, has an important influence on the effective forces for larger distances which are considerably reduced as compared to the prediction based on the primitive model. When these forces are repulsive, the long-distance behaviour can be described by an effective Yukawa pair potential with a solvent-renormalized charge. As a function of colloidal volume fraction and added salt concentration, this solvent-renormalized charge behaves qualitatively similar to that obtained via the Poisson-Boltzmann cell model but there are quantitative differences. For divalent counterions and nano-sized colloids, on the other hand, the hydration may lead to overscreened colloids with mutual attraction while the primitive model yields repulsive forces. All these new effects can be accounted for through a solvent-averaged primitive model (SPM) which is obtained from the full model by integrating out the solvent degrees of freedom. The SPM was used to access larger colloidal particles without simulating the solvent explicitly.Comment: 14 pages, 16 craphic