Use of very high-resolution airborne images to analyse 3d canopy architecture of a vineyard

Differencing between green cover and grape canopy is a challenge for vigour status evaluation in viticulture. This paper presents the acquisition methodology of very high-resolution images (4 cm), using a Sensefly Swinglet CAM unmanned aerial vehicle (UAV) and their processing to construct a 3D digital surface model (DSM) for the creation of precise digital terrain models (DTM). The DTM was obtained using python processing libraries. The DTM was then subtracted to the DSM in order to obtain a differential digital model (DDM) of a vineyard. In the DDM, the vine pixels were then obtained by selecting all pixels with an elevation higher than 50 [cm] above the ground level. The results show that it was possible to separate pixels from the green cover and the vine rows. The DDM showed values between −0.1 and + 1.5 [m]. A manually delineation of polygons based on the RGB image belonging to the green cover and to the vine rows gave a highly significant differences with an average value of 1.23 [m] and 0.08 [m] for the vine and the ground respectively. The vine rows elevation is in good accordance with the topping height of the vines 1.35 [m] measured on the field. This mask could be used to analyse images of the same plot taken at different times. The extraction of only vine pixels will facilitate subsequent analyses, for example, a supervised classification of these pixels

Augmenting a convolutional neural network with local histograms ::a case study in crop classification from high-resolution UAV imagery

The advent of affordable drones capable of taking high resolution images of agricultural fields creates new challenges and opportunities in aerial scene understanding. This paper tackles the problem of recognizing crop types from aerial imagery and proposes a new hybrid neural network architecture which combines histograms and convolutional units. We evaluate the performance of the proposed model on a 23-class classification task and compare it to other models. The result is an improvement of the classification performance

Two different mechanisms of stabilization of regular pi-stacks of radicals in switchable dithiazolyl-based materials

Materials based on regular π-stacks of planar organic radicals are intensively pursued by virtue of their technologically relevant properties. Yet, these π-stacks are commonly unstable against π-dimerization. In this computational study, we reveal that regular π-stacks of planar dithiazolyl radicals can be rendered stable, in some range of temperatures, via two different mechanisms. When the radicals of a π-stack are both longitudinally and latitudinally slipped with respect to each other, the corresponding regular π-stacked configuration is associated with a locally stable minimum in the potential energy surface of the system. Conversely, those regular π-stacks in which radicals are latitudinally slipped with respect to each other are stable as a result of a dynamic interconversion between two degenerate dimerized configurations. The existence of two stabilization mechanisms, which can be traced back to the bonding properties of isolated π-dimers, translates into two different ways of exploiting spin-Peierls-like transitions in switchable dithiazolyl-based materials

Early Dark Energy at High Redshifts: Status and Perspectives

Early dark energy models, for which the contribution to the dark energy density at high redshifts is not negligible, influence the growth of cosmic structures and could leave observable signatures that are different from the standard cosmological constant cold dark matter ($\Lambda$CDM) model. In this paper, we present updated constraints on early dark energy using geometrical and dynamical probes. From WMAP five-year data, baryon acoustic oscillations and type Ia supernovae luminosity distances, we obtain an upper limit of the dark energy density at the last scattering surface (lss), $\Omega_{\rm EDE}(z_{\rm lss})<2.3\times10^{-2}$ (95% C.L.). When we include higher redshift observational probes, such as measurements of the linear growth factors, Gamma-Ray Bursts (GRBs) and Lyman-$\alpha$ forest (\lya), this limit improves significantly and becomes $\Omega_{\rm EDE}(z_{\rm lss})<1.4\times10^{-3}$ (95% C.L.). Furthermore, we find that future measurements, based on the Alcock-Paczy\'nski test using the 21cm neutral hydrogen line, on GRBs and on the \lya forest, could constrain the behavior of the dark energy component and distinguish at a high confidence level between early dark energy models and pure $\Lambda$CDM. In this case, the constraints on the amount of early dark energy at the last scattering surface improve by a factor ten, when compared to present constraints. We also discuss the impact on the parameter $\gamma$, the growth rate index, which describes the growth of structures in standard and in modified gravity models.Comment: 11 pages, 9 figures and 4 table

The High Redshift Integrated Sachs-Wolfe Effect

In this paper we rely on the quasar (QSO) catalog of the Sloan Digital Sky Survey Data Release Six (SDSS DR6) of about one million photometrically selected QSOs to compute the Integrated Sachs-Wolfe (ISW) effect at high redshift, aiming at constraining the behavior of the expansion rate and thus the behaviour of dark energy at those epochs. This unique sample significantly extends previous catalogs to higher redshifts while retaining high efficiency in the selection algorithm. We compute the auto-correlation function (ACF) of QSO number density from which we extract the bias and the stellar contamination. We then calculate the cross-correlation function (CCF) between QSO number density and Cosmic Microwave Background (CMB) temperature fluctuations in different subsamples: at high z>1.5 and low z<1.5 redshifts and for two different choices of QSO in a conservative and in a more speculative analysis. We find an overall evidence for a cross-correlation different from zero at the 2.7\sigma level, while this evidence drops to 1.5\sigma at z>1.5. We focus on the capabilities of the ISW to constrain the behaviour of the dark energy component at high redshift both in the \LambdaCDM and Early Dark Energy cosmologies, when the dark energy is substantially unconstrained by observations. At present, the inclusion of the ISW data results in a poor improvement compared to the obtained constraints from other cosmological datasets. We study the capabilities of future high-redshift QSO survey and find that the ISW signal can improve the constraints on the most important cosmological parameters derived from Planck CMB data, including the high redshift dark energy abundance, by a factor \sim 1.5.Comment: 20 pages, 18 figures, and 7 table

Eliciting the Demand for Long Term Care Coverage: A Discrete Choice Modelling Analysis

We evaluate the demand for long term care (LTC) insurance prospects in a stated preference context, by means of the results of a choice experiment carried out on a representative sample of the Emilia-Romagna population. Choice modelling techniques have not been used yet for studying the demand for LTC services. In this paper these methods are first of all used in order to assess the relative importance of the characteristics which define some hypothetical insurance programmes and to elicit the willingness to pay for some LTC coverage prospects. Moreover, thanks to the application of a nested logit specification with partial degeneracy, we are able to model the determinants of the preference for status quo situations where no systematic cover for LTC exists. On the basis of this empirical model, we test for the effects of a series of socio-demographic variables as well as personal and household health state indicators