3D Simulation with virtual stereo rig for optimizing centrifugal fertilizer spreading

Stereovision can be used to characterize of the fertilizer centrifugal spreading process and to control the spreading fertilizer distribution pattern on the ground reference. Fertilizer grains, however, resemble each other and the grain images contain little information on texture. Therefore, the accuracy of stereo matching algorithms in literature cannot be used as a reference for stereo images of fertilizer grains. In order to evaluate stereo matching algorithms applied to images of grains a generator of synthetic stereo particle images is presented in this paper. The particle stereo image generator consists of two main parts: the particle 3D position generator and the virtual stereo rig. The particle 3D position generator uses a simple ballistic flight model and the disc characteristics to simulate the ejection and the displacement of grains. The virtual stereo rig simUlates the stereo acquisition system and generates stereo images, a disparity map and an occlusion map. The results are satisfying and present an accurate reference to evaluate stereo particles matching algorithms

Evaluation de la répartition d'engrais au sol par analyse d'images rapides lors de l'épandage centrifuge d'engrais

L’application de l’image à l’agriculture décrite dans cet article touche un domaine peu connu, à savoir l’épandage centrifuge d’engrais. Pour caractériser précisément cette intervention agricole, deux paramètres doivent être étudiés et en particulier la répartition d’engrais au sol.Although mechanically simple, centrifugal spreaders used for mineral fertilization involve not well-known complex physics phenomenons. To avoid bad fertilizer inputs in the field, the centrifugal spreading technique and particularly the initial conditions of flight of the granules have to be precisely understood. This paper presents the conception of a fast image processing system for the characterization of the centrifugal fertilizer spreading. This patented multiexposure system allows to determine granule trajectories after their ejection, by using a high resolution digital camera combined with a set of flashes, and different motion estimation methods. The results are then used in a ballistic model to obtain the fertilizer repartition on the ground.Bien que mécaniquement simples, les distributeurs centrifuges utilisés pour la fertilisation minérale impliquent des phénomènes physiques complexes qui ne sont pas actuellement caractérisés complètement. Pour éviter des mauvais apports d'engrais dans le champ, l'épandage centrifuge, et particulièrement les conditions initiales d'éjection des granulés, doivent être précisément comprises. Ce papier présente la conception d'un système d'acquisition d'images rapides pour la caractérisation de la répartition d'engrais au sol. Ce système multiexposition breveté permet de déterminer les trajectoires des granulés après leur éjection, en utilisant une caméra numérique haute résolution combinée à une batterie de flashes et différentes méthodes d'estimation du mouvement. Les résultats obtenus sont ensuite utilisés dans un modèle de vol balistique pour obtenir la répartition d'engrais au sol

3D image acquisition system based on shape from focus technique

agent Agrosup Dijon de l'UMREcolDurGEAPSIThis paper describes the design of a 3D image acquisition system dedicated to natural complex scenes composed of randomly distributed objects with spatial discontinuities. In agronomic sciences, the 3D acquisition of natural scene is difficult due to the complex nature of the scenes. Our system is based on the Shape from Focus technique initially used in the microscopic domain. We propose to adapt this technique to the macroscopic domain and we detail the system as well as the image processing used to perform such technique. The Shape from Focus technique is a monocular and passive 3D acquisition method that resolves the occlusion problem affecting the multi-cameras systems. Indeed, this problem occurs frequently in natural complex scenes like agronomic scenes. The depth information is obtained by acting on optical parameters and mainly the depth of field. A focus measure is applied on a 2D image stack previously acquired by the system. When this focus measure is performed, we can create the depth map of the scene

Conception d'un dispositif d'acquisition d'images agronomiques 3D en extérieur et développement des traitements associés pour la détection et la reconnaissance de plantes et de maladies

Dans le cadre de l'acquisition de l'information de profondeur de scènes texturées, un processus d'estimation de la profondeur basé sur la méthode de reconstruction 3D Shape from Focus est présenté dans ce manuscrit. Les deux étapes fondamentales de cette approche sont l'acquisition de la séquence d'images de la scène par sectionnement optique et l'évaluation de la netteté locale pour chaque pixel des images acquises. Deux systèmes d'acquisition de cette séquence d'images sont présentés ainsi que les traitements permettant d'exploiter celle-ci pour la suite du processus d'estimation de la profondeur. L'étape d'évaluation de la netteté des pixels passe par la comparaison des différents opérateurs de mesure de netteté. En plus des opérateurs usuels, deux nouveaux opérateurs basés sur les descripteurs généralisés de Fourier sont proposés. Une méthode nouvelle et originale de comparaison est développée et permet une analyse approfondie de la robustesse à différents paramètres des divers opérateurs. Afin de proposer une automatisation du processus de reconstruction, deux méthodes d'évaluation automatique de la netteté sont détaillées. Finalement, le processus complet de reconstruction est appliqué à des scènes agronomiques, mais également à une problématique du domaine de l'analyse de défaillances de circuits intégrés afin d'élargir les domaines d'utilisationIn the context of the acquisition of depth information for textured scenes, a depth estimation process based on a 3D reconstruction method called "shape from focus" is proposed in this thesis. The two crucial steps of this approach are the image sequence acquisition of the scene by optical sectioning and the local sharpness evaluation for each pixel of the acquired images. Two acquisition systems have been developed and are presented as well as different image processing techniques that enable the image exploitation for the depth estimation process. The pixel sharpness evaluation requires comparison of different focus measure operators in order to determine the most appropriate ones. In addition to the usual focus measure operators, two news operators based on generalized Fourier descriptors are presented. A new and original comparison method is developped and provides a further analysis of the robustness to various parameters of the focus measure operators. In order to provide an automatic version of the reconstruction process, two automatic sharpness evaluation methods are detailed. Finally, the whole reconstruction process is applied to agronomic scenes, but also to a problematic in failure analysis domain aiming to expand to other applicationsDIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Remote and proximal sensing for precision agriculture and viticulture". Special issue

International audienceSpecial Issue InformationDear Colleagues,Remote and proximal sensing are the two most common techniques concerning the acquisition of information about an object or any phenomenon without physical contact with the object. Remote sensing is widely tied to the use of satellite, airborne or UAV platforms using multi- or hyperspectral imagery. In terms of proximal sensing, the sensor is close to the object and is installed on platforms ranging from handheld, fixed installations, or robotics and tractor-embedded sensors. The types of sensors range from simple RGB or grey-level-cameras to multispectral and hyperspectral high resoluted imaging systems or even thermographic camera.Associated with plant growth conditions and phenotyping techniques, remote and proximal sensing are able to provide information on nutrient deficiency, biotic stress such as pests and diseases as well as abiotic stresses, allowing Precision Agriculture and Viticulture practices.We invite thus papers on both fundamental and applied research relating on Remote and Proximal Sensing for Precision Agriculture and Viticulture, combining spectral, spatial and temporal information based on multi- and hyperspectral imagery with the capabilities of management-oriented crop simulation models. We also invite papers dedicated to new sensors able to be used in Agriculture; aiming at a better management of the crops, and methods for better crop management and more respectful of the environment.Dr. Frédéric CointaultGuest Edito

Image acquisition and processing for precision farming applications

International audienceInitially developed for technical industrial sectors such as medicine or aeronautics, imaging technics are more and more used since 30 years in agriculture and viticulture. The development of acquisition tool and the decreasing of the calculation time allowed using imagery in laboratory under controlled conditions. At the beginning of the 90’s, the concept of Precision Farming has been developed in the USA, considering a field as a heterogeneous area needing different input in terms of fertiliser or protection product. In the same time, the aperture of the GPS system for civil applications has allowed the development of remote sensing domain. Combining GPS information and imagery conducted also to the emergence of proxy-detection applications, in agriculture and viticulture domains, in order to optimize crop management. A localized crop management needs the use of new technologies such as computing, electronics and imaging, and the conception of a proxy-detection system is motivated by the need of better resolution, precision, temporality and lower cost, compared to remote sensing. The use of computer vision techniques allows obtaining this information automatically with objective measurements compared with visual or manual acquisition. The main applications covered by the computer vision in agriculture are tied to the crop characterization (biomass estimation, leaf area, volume, height of the crop, disease determination etc), the aerial or root phenotyping in the fields or in specific platforms and the understanding of spraying and spreading processes. This presentation will explain the different imaging systems used to characterize the previous parameters, in 2D or 3D. It will also give some details on the dedicated image processing methods developed, related to motion estimation, focus information, pattern recognition and multi –hyper spectral data