Christelle Gée

Cluster isolated chemical reactions

The study of chemical reactions in clusters is a rapidly growing field that is attractive for investigating medium effects in reaction dynamics. Cluster isolated chemical reaction (CICR) is a new direction developed in our laboratory, which enables quantitative studies to be made in that field. CICR experiments consist in depositing a controlled number of reactants on large van der Waals clusters that play the role of a solvent of fairly well known size, structure and temperature. This offers enormous advantages, both on the experimental side and for further theoretical investigations. The present review intends to draw together informations that are pertinent for developing experiments and concepts towards understanding chemical reactions in clusters, through CICR-type experiments. In particular, it reviews the questions first of generating and controlling the properties of large van der Waals clusters and secondly of attaching reactants to clusters and controlling their number and location in the cluste...

Cluster isolated chemical reactions: Reactivity of Ba atoms and small Ba clusters with SF6 and CO2 molecules on large argon clusters

The cluster isolated chemical reaction technique was used to investigate the reactivity of the Ba/CO2 and Ba/SF6 systems in the environment of Ar≈6000 clusters. The method was extended to document several aspects of the reactivity. Notably, mass spectrometry gives insight into the full reactivity of the system deposited on the clusters. Laser induced fluorescence (LIF) and chemiluminescence are also used as detection tools. Unexpectedly, we found that a single barium atom neither reacts with CO2 nor with SF6 at the cluster temperature (32 K). In contrast, the LIF results suggest the formation of a weakly bound covalent Ba…CO2 complex. Finally, Ba2 and larger barium aggregates react with CO2, and Ba3 and larger aggregates react with SF6. The chemiluminescent products are Ba2O in the first case, and BaF in the second. These observations are rationalized on the ground of the harpoon model.

Dynamics of the deactivation and desorption of Ba atoms from Ar clusters

The Doppler profiles of Ba(3P2) atoms desorbed from the surface of argon clusters following the deactivation of Ba(1P1) have been measured. These measurements have been performed for desorption from pure ArN clusters and as a function of a known average number of CH4 molecules deposited on the cluster. Analysis of the profile widths with respect to the kinetic energy release from deactivation indicates that desorption occurs along a single Ba–Ar and Ba–CH4 coordinate in the former and latter cases, respectively. By comparing the kinetic energy distributions in the desorbed barium with the relative kinetic energy available at the temperature of the cluster it is found that the collisions leading to deactivation in both cases are gas kinetic at the temperature of the cluster (35 K). The residual anisotropies in the Doppler profiles reveal the Ba–Ar deactivation to be a relatively inefficient process allowing the barium to undertake a full migration on the cluster surface before desorbing. This results in an...

Field radiometric calibration of a multispectral on-the-go sensor dedicated to the characterization of vineyard foliage

Development of a multispectral on-the-go system (visible and NIR) to characterize vineyard in natural light.Implementation of a radiometric method to calibrate the images using linear and spatial interpolation.Computation of vegetation index (NDVI) from reflectance images and strong correlation with Greenseeker data.Proximal multispectral imaging system can be used to assess foliage vigor at green berry stage. The accurate assessment of the vigor and disease impact is a major challenge in precision viticulture. It is essential for managing phytosanitary treatments. Up to now, some remote sensing techniques such as aerial imagery and handheld optical sensors have been applied to grapevine characterization. However each technique provides limited, specific information about foliage. To broaden the characterization of the foliage, we developed a proximal integrated, multispectral imaging sensor that operates in the visible and near-infrared bands. It is mounted on a track-laying tractor equipped with a Greenseeker-RT-100, coupled with a GPS-RTK. As the sensor is very sensitive to the ambient light, a radiometric calibration is required: it allows producing absolute reflectance images, using a color chart. If the chart is hidden by leaves, for instance, the images are corrected using the linear interpolation method. The adaptive radiometric method is evaluated as a function of the number of neutral patches selected on the color chart during the linear regression process and the efficiency of the spatial interpolation method is assessed using a leave-one-out-cross-validation (LOOCV) method.The radiometric calibration is validated by comparison of NDVI maps produced by imagery and by the Greenseeker, a commercial system. In the early stage of berry formation, we examined and quantified the spatial patterns and demonstrated a low-cost imagery method that is capable of analyzing correctly the vigor. This corroborates the efficiency of the calibration method encouraging the use of multi-spectral imagery for other vineyard applications, such as the characterization of physiological status.

Crop/weed discrimination in simulated images

In the context of site-specific weed management by vision systems, an efficient image processing for a crop/weed discrimination is required in order to quantify the Weed Infestation Rate (WIR) in an image. This paper presents a modeling of crop field in presence of different Weed Infestation Rates and a set of simulated agronomic images is used to test and validate the effectiveness of a crop/weed discrimination algorithm. For instance, an algorithm has been implemented to firstly detect the crop rows in the field by the use of a Hough Transform and secondly to detect plant areas by a region based-segmentation on binary images. This image processing has been tested on virtual cereal fields of a large field of view with perspective effects. The vegetation in the virtual field is modeled by a sowing pattern for crop plants and the weed spatial distribution is modeled by either a Poisson process or a Neyman-Scott cluster process. For each simulated image, a comparison between the initial and the detected weed infestation rate allows us to assess the accuracy of the algorithm. This comparison demonstrates an accuracy of better than 80% is possible, despite that intrarow weeds can not be detected from this spatial method.

Chemiluminescent Ba + N2O reaction in molecular clusters of CH4 and mixed ArCH4 clusters

Abstract We report measurements of visible chemiluminescence from a molecular cluster environment. Chemiluminescence spectra of the Ba + N 2 O reaction on pure CH 4 clusters and on Ar clusters with a small fraction of CH 4 have been measured and compared. As in the case of pure Ar clusters, there is a spectral contribution from rovibrationally ‘hot’ BaO ∗ , which is ejected immediately after the reaction, and a contribution from ‘cold’ solvated BaO ∗ . When using pure CH 4 clusters the vibrational structure of the latter is washed out and a considerable blue-shift observed. From mixed Ar CH 4 clusters a superposition of pure cluster spectra is observed, and the blue-shift of the component influenced by CH 4 is smaller than on pure CH 4 clusters. This is interpreted by the formation of a still-luminescent BaO ∗ CH 4 complex solvated on the Ar cluster.

Detecting crops and weeds in precision agriculture

In recent decades, precision agriculture, a practice geared to delivering ‘the right dose at the right place at the right moment,’ has become possible with the development of remote sensors. In particular, spatial vegetative heterogeneity within fields can be digitally recorded using vision systems embedded either in agricultural engines or in small remotely piloted aircraft. These images can then be used to identify regions of high weed content to focus, and reduce, herbicide applications. We previously developed two programs based on such systems for weed detection in crop fields. A multispectral camera embedded in an aircraft (see Figure 1) enabled us to obtain images within which the spatial distribution of weeds can be estimated. Various classification methods (ANN, k-nearest neighbor) were used to categorize pixels as either crop or weed.1, 2 Alternatively, the second experimental system incorporates an imaging system in a real-time precision sprayer (see Figure 2). For instance, from a monochrome CCD camera located in front of the tractor, the discrimination between crops and weeds is obtained using a 2D Gabor filtering process based on spatial information.3 This method allows us to generate a weed infestation map under the assumption that the periodic signal is associated with crop rows. A pinhole camera model is then used to translate the weed patch coordinates from the image into real-world coordinates to time the triggering of a series of actuators fixed in front of each nozzle on the spraying boom. However, few manual ground assessments have been done to evaluate the performance of these image-processing techniques. Hence, a method to validate the potential of such crop/weed discrimination algorithms is needed. When the initial parameters of the scene (crop and weed locations, weed infestation rates) are known, it is possible to assess and compare the efficiency of crop/weed discrimination algorithms such as Gabor filtering, the Hough transform, and the wavelet transform. Figure 1. A four filter wheel camera with filters in the blue, green, red and infrared ranges is embedded in a small remotely piloted aircraft.

Unsupervised Classification Algorithm for Early Weed Detection in Row-Crops by Combining Spatial and Spectral Information

In agriculture, reducing herbicide use is a challenge to reduce health and environmental risks while maintaining production yield and quality. Site-specific weed management is a promising way to reach this objective but requires efficient weed detection methods. In this paper, an automatic image processing has been developed to discriminate between crop and weed pixels combining spatial and spectral information extracted from four-band multispectral images. Image data was captured at 3 m above ground, with a camera (multiSPEC 4C, AIRINOV, Paris) mounted on a pole kept manually. For each image, the field of view was approximately 4 m × 3 m and the resolution was 6 mm/pix. The row crop arrangement was first used to discriminate between some crop and weed pixels depending on their location inside or outside of crop rows. Then, these pixels were used to automatically build the training dataset concerning the multispectral features of crop and weed pixel classes. For each image, a specific training dataset was used by a supervised classifier (Support Vector Machine) to classify pixels that cannot be correctly discriminated using only the initial spatial approach. Finally, inter-row pixels were classified as weed and in-row pixels were classified as crop or weed depending on their spectral characteristics. The method was assessed on 14 images captured on maize and sugar beet fields. The contribution of the spatial, spectral and combined information was studied with respect to the classification quality. Our results show the better ability of the spatial and spectral combination algorithm to detect weeds between and within crop rows. They demonstrate the improvement of the weed detection rate and the improvement of its robustness. On all images, the mean value of the weed detection rate was 89% for spatial and spectral combination method, 79% for spatial method, and 75% for spectral method. Moreover, our work shows that the plant in-line sowing can be used to design an automatic image processing and classification algorithm to detect weed without requiring any manual data selection and labelling. Since the method required crop row identification, the method is suitable for wide-row crops and high spatial resolution images (at least 6 mm/pix).

Development of methods based on double Hough transform or Gabor filtering to discriminate between crop and weed in agronomic images

This paper presents two spatial methods to discriminate between crop and weeds. The application is related to agronomic image with perspective crop rows. The first method uses a double Hough Transform permitting a detection of crop rows and a classification between crop and weeds. The second method is based on Gabor filtering, a band pass filter. The parameters of this filter are detected from a Fast Fourier Transform of the image. For each method, a weed infestation rate is obtained. The two methods are compared and a discussion concludes about the abilities of these methods to detect the crop rows in agronomic images. Finally, we discuss this method regarding the capability of the spatial approach for classifying weeds from crop.

Metal–acetylene binding in gaseous WC2H2+

Abstract The binding energy between W + and C 2 H 2 in gaseous WC 2 H 2 + has been investigated by photodissociation in the cell of a Fourier transform ion cyclotron resonance spectrometer, and by quantum-chemical calculations. It is found that the only low-energy structure of WC 2 H 2 + is a metallacyclopropene, which dissociates only to bare W + and C 2 H 2 under visible irradiation. The binding energy determined both experimentally and theoretically is 3.3±0.4 eV, significantly larger than any of the previously determined metal–acetylene bond energies in the gas phase. The experimental value is determined from both single- and two-photon dissociation thresholds. No absorption is detected below 1.5 eV of photon energy, in reasonable agreement with the first allowed transitions being calculated at 1.1 and 1.5 eV.