0000000000939925

AUTHOR

Frédéric Cointault

showing 53 related works from this author

Systèmes imageurs 3D pour des applications agricoles : caractérisation de cultures et phénotypage de racines

2016

The development of the concepts of precision agriculture and viticulture since the last three decades has shown the need to use first 2D image acquisition techniques and dedicated image processing. More and more needs concern now 3D images and information. The main ideas of this chapter is thus to present some innovations of the 3D tools and methods in the agronomic domain. This chapter will particularly focus on two main subjects such as the 3D characterization of crop using Shape from Focus or Structure from Motion techniques and the 3D use for root phenotyping using rhizotron system. Results presented show that 3D information allows to better characterize crucial crop morphometric parame…

[SDE] Environmental Sciences0106 biological sciences2. Zero hungerRoot (linguistics)Focus (computing)SHAPE FROM FOCUSComputer scienceMachine vision3D reconstructionImage processing04 agricultural and veterinary sciencesPHENOTYPAGE15. Life on land01 natural sciencesData scienceDomain (software engineering)Agricultural science[SDE]Environmental Sciences040103 agronomy & agriculture0401 agriculture forestry and fisheriesStructure from motionSTRUCTURE FROM MOTIONPrecision agriculture010606 plant biology & botany
researchProduct

3D image acquisition system based on shape from focus technique

2013

agent Agrosup Dijon de l'UMREcolDurGEAPSI; This 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…

Engineering[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[SDV]Life Sciences [q-bio]ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONscenesImage processingagronomic scenes[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyClassification of discontinuitieslcsh:Chemical technologyBiochemistryArticleAnalytical ChemistryDomain (software engineering)shape from focusDepth map0202 electrical engineering electronic engineering information engineeringagronomiclcsh:TP1-1185Computer vision3D image acquisition system;shape from focus;focus measure;agronomic;scenesDepth of fieldElectrical and Electronic EngineeringInstrumentationComputingMethodologies_COMPUTERGRAPHICS3D image acquisition systemfocus measureMonocular[ SDV ] Life Sciences [q-bio]business.industry3D image acquisition system; shape from focus; focus measure; agronomic scenesScene statisticsDistributed object021001 nanoscience & nanotechnologyAtomic and Molecular Physics and Optics020201 artificial intelligence & image processingArtificial intelligence0210 nano-technologybusiness
researchProduct

Protocol for the Definition of a Multi-Spectral Sensor for Specific Foliar Disease Detection: Case of “Flavescence Dorée”

2018

Flavescence Doree (FD) is a contagious and incurable grapevine disease that can be perceived on leaves. In order to contain its spread, the regulations obligate winegrowers to control each plant and to remove the suspected ones. Nevertheless, this monitoring is performed during the harvest and mobilizes many people during a strategic period for viticulture. To solve this problem, we aim to develop a Multi-Spectral (MS) imaging device ensuring an automated grapevine disease detection solution. If embedded on a UAV, the tool can provide disease outbreaks locations in a geographical information system allowing localized and direct treatment of infected vines. The high-resolution MS camera aims…

0106 biological sciences[SDE] Environmental SciencesDisease detectionComputer science[SDV]Life Sciences [q-bio]Multispectral imageradiometric/geometric correctionsFeature selectionMulti spectral01 natural sciencesfeature selection[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologytexture analysisProtocol (science)Artificial neural networkbusiness.industrymultispectral sensorOutbreakPattern recognition04 agricultural and veterinary sciencesFlavescence Dorée3. Good health[SDV] Life Sciences [q-bio]Identification (information)classification[SDE]Environmental Sciences040103 agronomy & agriculture0401 agriculture forestry and fisheriesFlavescence doréeArtificial intelligencebusiness010606 plant biology & botany
researchProduct

Comparison of leaf surface roughness analysis methods by sensitivity to noise analysis

2015

International audience; Surface roughness is of great interest in agricultural spraying because it is used to characterise leaf surface wettability to predict the behaviour of droplets on a leaf surface. In recent years, the use of texture analysis to estimate surface roughness has emerged. In this paper we propose to estimate leaf surface roughness by using an optimisation of the Generalized Fourier Descriptors method. This approach is then compared with two other standard methods in the literature, one based on grey level intensity variation and the other on wavelet decomposition. Since roughness has many definitions and each method is calculated differently, we propose a new approach to …

Surface (mathematics)Materials science[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingGaussianSoil ScienceWavelet decompositionSurface finishLeaf roughnessNoise analysissymbols.namesakeOptics[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingSurface roughnessSensitivity (control systems)Generalized Fourier DescriptorsSensitivity indicatorbusiness.industryOptical roughnessNoiseControl and Systems EngineeringsymbolsWettingBiological systembusinessAgronomy and Crop ScienceIntensity (heat transfer)Food Science
researchProduct

12 - Feasability study of a wheatears counting system per vision

2005

Working on a feasibility study of wheatears counting, a colour component texture's analysis method was developed. The agronomic goal is yield prediction before harvest evaluating mean number of wheatears per squared meter according to the field variation knowledge. To this counting system, we evaluate six textural parameters (two statistical parameters and four Haralick features from co-occurrence matrix) on the main colour systems and vegetation indices used in agronomic applications. A new hybrid system provides a representation of wheatears' pictures taken under natural conditions with a better extraction of wheat. A method based on distances measurements (Euclidian, Mahalanobis) allows …

[SDV] Life Sciences [q-bio][SDV]Life Sciences [q-bio]
researchProduct

Statistical methods for texture analysis applied to agronomical images

2008

For activities of agronomical research institute, the land experimentations are essential and provide relevant information on crops such as disease rate, yield components, weed rate... Generally accurate, they are manually done and present numerous drawbacks, such as penibility, notably for wheat ear counting. In this case, the use of color and/or texture image processing to estimate the number of ears per square metre can be an improvement. Then, different image segmentation techniques based on feature extraction have been tested using textural information with first and higher order statistical methods. The Run Length method gives the best results closed to manual countings with an averag…

business.industryFeature extractionPattern recognitionImage processingImage segmentationTexture (music)Class (biology)Image (mathematics)Image textureCluster validity indexComputer visionArtificial intelligencebusinessMathematicsImage Processing: Machine Vision Applications
researchProduct

Roughness evaluation of vine leaf by image processing

2013

International audience; The study of leaf surface roughness is very important in the domain of precision spraying. It is one of the parameters that allow to reduce costs and losses of phytosanitary prod- ucts and to improve the spray accuracy. Moreover, the leaf roughness is related to adhesion mechanisms of liquid on a surface. It can be used to define leaf nature surface (hy- drophilic/hydrophobic). The main goal of this study is thus to estimate and to follow the evolution of leaf roughness using image processing and computer vision. The develop- ment and application of computer vision for measurement of surface leaf roughness using artificial neural networks will be described. The syste…

[ MATH ] Mathematics [math]0106 biological sciences0209 industrial biotechnologyScanning electron microscope[SDV]Life Sciences [q-bio]Computer Vision[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[MATH] Mathematics [math]02 engineering and technologySurface finishLeaf roughness01 natural sciences[PHYS] Physics [physics][SPI]Engineering Sciences [physics]020901 industrial engineering & automation[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ SPI ] Engineering Sciences [physics]Surface roughnessComputer vision[MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS[PHYS]Physics [physics][ PHYS ] Physics [physics]Artificial neural network[STAT]Statistics [stat]Multilayer perceptron[SDE]Environmental SciencesBiological system[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMaterials science[ STAT ] Statistics [stat][INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SPI] Engineering Sciences [physics]IASTEDFast Fourier transformNeural NetworkImage processingImage processing[SDV.BV]Life Sciences [q-bio]/Vegetal BiologyTexturelanguage technologies[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingPrecision agriculturebusiness.industry[STAT] Statistics [stat]Precision agricultureArtificial intelligencebusiness010606 plant biology & botany
researchProduct

Optimizing Hough transform for fertilizer spreading optical control

2006

International audience; In Europe, centrifugal spreading is a widely used method for agricultural soil fertilization. In this broadcasting method, fertilizer particles fall onto a spinning disk, are accelerated by a vane, and afterward are ejected into the field. To predict and control the spread pattern, a low-cost, embeddable technology adapted to farm implements must be developed. We focus on obtaining the velocity and the direction of fertilizer granules when they begin their flight by means of a simple imaging system. We first show that the outlet angle of the vane is a bounded value and that its measurement provides the outlet velocity of the particle. Consequently, a simple camera un…

[SDV.SA]Life Sciences [q-bio]/Agricultural sciencesComputer sciencebusiness.industry[SDV]Life Sciences [q-bio]General EngineeringImage processing04 agricultural and veterinary sciences02 engineering and technologyImage segmentationAtomic and Molecular Physics and OpticsHough transformlaw.inventionDigital imagelaw040103 agronomy & agriculture0202 electrical engineering electronic engineering information engineeringSpreading optical control0401 agriculture forestry and fisheriesCentrifugal spreading020201 artificial intelligence & image processingComputer visionArtificial intelligenceQuantization (image processing)businessFocus (optics)
researchProduct

Texture Classification with Generalized Fourier Descriptors in Dimensionality Reduction Context: An Overview Exploration

2008

In the context of texture classification, this article explores the capacity and the performance of some combinations of feature extraction, linear and nonlinear dimensionality reduction techniques and several kinds of classification methods. The performances are evaluated and compared in term of classification error. In order to test our texture classification protocol, the experiment carried out images from two different sources, the well known Brodatz database and our leaf texture images database.

Computer sciencebusiness.industryDimensionality reductionFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONNonlinear dimensionality reductionPattern recognitionContext (language use)Texture (geology)Term (time)symbols.namesakeFourier transformsymbolsArtificial intelligencebusiness
researchProduct

Texture analysis with statistical methods for wheat ear extraction

2007

In agronomic domain, the simplification of crop counting, necessary for yield prediction and agronomic studies, is an important project for technical institutes such as Arvalis. Although the main objective of our global project is to conceive a mobile robot for natural image acquisition directly in a field, Arvalis has proposed us first to detect by image processing the number of wheat ears in images before to count them, which will allow to obtain the first component of the yield. In this paper we compare different texture image segmentation techniques based on feature extraction by first and higher order statistical methods which have been applied on our images. The extracted features are…

Transform theoryComputer sciencebusiness.industryFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionMobile robotImage processingImage segmentationField (computer science)Image (mathematics)Component (UML)Computer visionArtificial intelligencebusinessEighth International Conference on Quality Control by Artificial Vision
researchProduct

Two-step cross correlation-based algorithm for motion estimation applied to fertilizer granules' motion during centrifugal spreading

2011

Imaging systems are progressing in both accuracy and ro- bustness, and their use in precision agriculture is increasing accordingly. One application of imaging systems is to understand and control the cen- trifugal fertilizing spreading process. Predicting the spreading pattern on the ground relies on an estimation of the trajectories and velocities of ejected granules. The algorithms proposed to date have shown low ac- curacy, with an error rate of a few pixels. But a more accurate estimation of the motion of the granules can be achieved. Our new two-step cross- correlation-based algorithm is based on the technique used in particle image velocimetry (PIV), which has yielded highly accurate…

fluid mechanicsImage processing01 natural sciences010305 fluids & plasmas010309 opticsmotion estimationMotion estimationcameras0103 physical sciencesComputer visionImage sensorMathematicsCross-correlationPixelbusiness.industrycentrifugesGeneral EngineeringfertilisersFluid mechanicsSubpixel renderingAtomic and Molecular Physics and Opticsimage processingParticle image velocimetryvelocimetersArtificial intelligencebusinessAlgorithm
researchProduct

Système de prise d'images haute résolution pour l'analyse de projection de particules : application à l'épandage centrifuge d'engrais

2002

This paper describes the design of a high resolution low cost imaging system for the analysis of high speed particle projection. This system, based on a camera and a set of flashes, is used to characterize the centrifugal spreading of fertilizer particles ejected at speeds of environ 30 m s. Multiexposure images collected with the camera installed perpendicular to the output flow of granules are analysed to estimate the trajectories of the fertilizer granules. Very good results are obtained with the Markov random fields method, in comparison with others.

[SDE] Environmental SciencesPhysicsRandom fieldMarkov chainbusiness.industryApplied MathematicsFlow (psychology)Resolution (electron density)04 agricultural and veterinary sciences02 engineering and technologyengineering.materialOptics[SDE]Environmental Sciences040103 agronomy & agriculture0202 electrical engineering electronic engineering information engineeringPerpendicularengineering0401 agriculture forestry and fisheriesParticle020201 artificial intelligence & image processingFertilizerbusinessProjection (set theory)InstrumentationEngineering (miscellaneous)Measurement Science and Technology
researchProduct

Development of a High Irradiance LED Configuration for Small Field of View Motion Estimation of Fertilizer Particles

2015

International audience; Better characterization of the fertilizer spreading process, especially the fertilizer pattern distribution on the ground, requires an accurate measurement of individual particle properties and dynamics. Both 2D and 3D high speed imaging techniques have been developed for this purpose. To maximize the accuracy of the predictions, a specific illumination level is required. This paper describes the development of a high irradiance LED system for high speed motion estimation of fertilizer particles. A spectral sensitivity factor was used to select the optimal LED in relation to the used camera from a range of commercially available high power LEDs. A multiple objective …

Technology and EngineeringLightIrradianceengineering.materiallcsh:Chemical technologyBiochemistryArticleAnalytical Chemistrylaw.inventionLED illuminationOpticsImaging Three-DimensionallawSPREADERMotion estimationGenetic algorithmRange (statistics)genetic algorithmlcsh:TP1-1185[ INFO.INFO-ES ] Computer Science [cs]/Embedded SystemsElectrical and Electronic EngineeringFertilizersInstrumentationLightingComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingbusiness.industryProcess (computing)AgricultureLED illumination; genetic algorithm; fertilizerLED illumination;genetic algorithm;fertilizerfertilizerAtomic and Molecular Physics and OpticsSIMULATIONSARRAYSSpectral sensitivityengineering[INFO.INFO-ES]Computer Science [cs]/Embedded SystemsFertilizerbusinessAERODYNAMIC PROPERTIES[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAlgorithmsLight-emitting diode
researchProduct

Associer biostimulants, SDP, systèmes d’imagerie et de pulvérisation au service de la santé du vignoble

2016

Prod?IPMGEAPSISPEEAINRAUBAGROSUPDOCT; Associer biostimulants, SDP, systèmes d’imagerie et de pulvérisation au service de la santé du vignoble

[SDV] Life Sciences [q-bio]SDP[ SDV ] Life Sciences [q-bio][SDV]Life Sciences [q-bio]vignebiostimulant
researchProduct

Biocontrôle et pulvérisation

2020

pulvérisation foliaire[SDE] Environmental Sciencesbiocontrôlevigne
researchProduct

In situ Phenotyping of Grapevine Root System Architecture by 2D or 3D Imaging: Advantages and Limits of Three Cultivation Methods

2021

International audience; The root system plays an essential role in the development and physiology of the plant, as well as in its response to various stresses. However, it is often insufficiently studied, mainly because it is difficult to visualize. For grapevine, a plant of major economic interest, there is a growing need to study the root system, in particular to assess its resistance to biotic and abiotic stresses, understand the decline that may affect it, and identify new ecofriendly production systems. In this context, we have evaluated and compared three distinct growing methods (hydroponics, plane, and cylindric rhizotrons) in order to describe relevant architectural root traits of …

0106 biological sciences0301 basic medicineRoot (linguistics)phenotypingContext (language use)Root systemPlant ScienceBiologyrhizotron01 natural sciencesSkeletonizationSB1-111003 medical and health sciencesCutting[SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agricultureMethods2. Zero hungerroot system architectureNeutron tomographyRhizotronPlant culture[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]15. Life on landHydroponicsgrapevine2D/3D imaging030104 developmental biologyroot traitsneutron tomographyBiological system010606 plant biology & botanyFrontiers in Plant Science
researchProduct

Characterisation of centrifugal spreaders : from collection trays to high speed stereovision

2016

AgrosupEAGEAPSICT1; Presenting Author Jürgen Vangeyte - ILVO, Agricultural Engineering, Belgium Description Precision fertilization requires accurate techniques for determining the spread pattern of centrifugal fertilizer spreaders. Traditionally, the spread pattern was determined by measuring the fertilizer distribution on the ground. Because such measurements are time consuming and complex, a high speed stereovision setup and dedicated image processing algorithms were developed for position and motion estimation of fertilizer grains leaving a centrifugal spreader. The measurements are combined with a ballistic flight model to predict the landing points of the individual grains and the act…

[SDV] Life Sciences [q-bio][ SDV ] Life Sciences [q-bio][SDV]Life Sciences [q-bio]
researchProduct

Solution de détection des maladies de la vigne par imagerie de drone. Diagnostic et réduction des pesticides à la parcelle

2016

ARTICLE A NE PAS DIFFUSEREAGEAPSIAgrosupCT1; Solution de détection des maladies de la vigne par imagerie de drone. Diagnostic et réduction des pesticides à la parcelle

réduction pesticides[SDV] Life Sciences [q-bio][ SDV ] Life Sciences [q-bio][SDV]Life Sciences [q-bio]imagerie aériennediagnosticdétection des maladiesdronevigne
researchProduct

L'utilisation de la proxi-détection 3D pour la caractérisation du blé

2012

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
researchProduct

Early detection of disease on leaves by image processing

2013

International audience

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal BiologyComputingMilieux_MISCELLANEOUS
researchProduct

Image acquisition and processing for precision farming applications

2015

Initially 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 …

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyimage acquisitionprecision farming applicationsimage processing
researchProduct

Mesure de netteté basée sur les descripteurs généralisés de Fourier appliquée à la reconstruction 3D

2013

National audience

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal BiologyComputingMilieux_MISCELLANEOUS
researchProduct

A 3-D stereovision simulator for centrifugal fertilizer granule spreading

2012

International audience

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental SciencesComputingMilieux_MISCELLANEOUS
researchProduct

Automatic analyzis of droplet impact by high speed imaging

2012

International audience; The impact of agricultural activities on the water quality is the consequence of the loss of fertilisers (chemical fertilisers, livestock effluent, also referred to as farm fertiliser, food-processing effluent and sludge) and crop treatment products (phytosanitary products). This pollution may prevent certain uses of water, notably its use for human and animal food (groundwater and surface water), and leads to a deterioration in aquatic environments. In the domain of vineyard precision spraying research, one of the most important objectives is to minimize the volume of phytosanitary products. It is also to be more environmentally respectful with more effective vine l…

high speed imaging;spraying application;weber number;active contoursprecision agriculture[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SDV]Life Sciences [q-bio][ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingimage processingactive contours[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingspraying application[SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biologyhigh speed imaging[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingWeber number[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
researchProduct

DAMAV project for vineyard disease detection by UAV imagery

2016

AgrosupBAPGEAPSI; DAMAV project for vineyard disease detection by UAV imagery . CIRG-AgEng 2016, International Conférence on Agricultural Engineering, Automation, Environment and Food Safety

[SDV] Life Sciences [q-bio][ SDV ] Life Sciences [q-bio][SDV]Life Sciences [q-bio]
researchProduct

Texture, color and frequential proxy-detection image processing for crop characterization in a context of Precision Agriculture

2012

[SDV.SA]Life Sciences [q-bio]/Agricultural sciences[SDV.SA] Life Sciences [q-bio]/Agricultural sciences[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
researchProduct

High Speed Stereovision Setup for Position and Motion Estimation of Fertilizer Particles Leaving a Centrifugal Spreader

2014

EA SPE GEAPSI; International audience; A 3D imaging technique using a high speed binocular stereovision system was developed in combination with corresponding image processing algorithms for accurate determination of the parameters of particles leaving the spinning disks of centrifugal fertilizer spreaders. Validation of the stereo-matching algorithm using a virtual 3D stereovision simulator indicated an error of less than 2 pixels for 90% of the particles. The setup was validated using the cylindrical spread pattern of an experimental spreader. A 2D correlation coefficient of 90% and a Relative Error of 27% was found between the experimental results and the (simulated) spread pattern obtai…

Technology and Engineering[SDV]Life Sciences [q-bio]Video RecordingCentrifugationlcsh:Chemical technologyArticleSPINNING DISC SPREADERmotion estimationMotionImaging Three-DimensionalArtificial Intelligencelcsh:TP1-1185[SPI.GPROC]Engineering Sciences [physics]/Chemical and Process EngineeringFertilizersstereovisionstereovision;motion estimation;fertilizer;centrifugal spreader[ SDV ] Life Sciences [q-bio][ SPI.GPROC ] Engineering Sciences [physics]/Chemical and Process EngineeringAgriculturefertilizerSIMULATION-MODELPATTERNcentrifugal spreaderentrifugal spreaderVELOCITIESRheologySensors
researchProduct

Measuring vine leaf roughness by image processing

2013

International audience; In precision spraying, spray application efficiency depends on the pesticide application method, the phytosanitary product as well as the leaf surface properties. For environmental and economic reasons, the global trend is to reduce the pesticide application rate of the few approved active substances. Under these constraints, one of the challenges is to improve the efficiency of pesticide application. Different parameters can influence on pesticide application as nozzle types, liquid viscosity and leaf surface. Specific models have been developed showing that the predominant factor for the leaf is the leaf roughness, because it is related on adhesion mechanisms of li…

[SDV] Life Sciences [q-bio][SDE] Environmental SciencesGeneralized Fourier Descriptor[SDV]Life Sciences [q-bio][SDE]Environmental SciencesNeural Network[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyleaf surface roughnessnonlinear reduction dimensionality methodstexture
researchProduct

Nodulated root imaging within the high throughput plant phenotyping platform (PPHD, INRA, Dijon)

2014

Présentation orale/ Communication avec actes

[SDE] Environmental Sciences[ SDV ] Life Sciences [q-bio][SPI] Engineering Sciences [physics][SDV]Life Sciences [q-bio][SDV] Life Sciences [q-bio]High Throughput Plant Phenotyping Platform (PPHD)[SPI]Engineering Sciences [physics]stomatognathic diseasesnodulated root imaging[SDE]Environmental Sciences[ SPI ] Engineering Sciences [physics][SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal BiologyComputingMilieux_MISCELLANEOUS
researchProduct

Imagerie pour les maladies de la vigne

2013

La viticulture est confrontée à des problèmes majeurs dont la lutte contre les maladies cryptogamiques telles que l’oïdium ou la pourriture grise. Elle utilise alors majoritairement des produits phytosanitaires de synthèse susceptibles de poser des problèmes environnementaux et sanitaires, et de favoriser la sélection de souches de microorganismes pathogènes résistantes. Dans le cadre du plan Ecophyto 2018, la recherche de méthodes visant à diminuer, voire à remplacer, les fongicides de synthèse au vignoble, a pris toute sa place. Deux approches peuvent alors être conduites pour répondre à cet objectif : 1) la réduction du nombre de traitements et des doses appliquées et 2) le développement…

[SDV] Life Sciences [q-bio][SDE] Environmental SciencesdiseaseBotrytis cinereaimage analysis[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyimage acquisitiongrapevine
researchProduct

Early detection of disease on leaves by image processing

2013

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biology
researchProduct

Measurement of the motion of fertilizer particles leaving a centrifugal spreader using a fast imaging system

2003

International audience; Although mechanically simple, centrifugal spreaders used for meneral fertilization involve complex physics that cannot be fully characterized at the present time. We are developing sensors to evaluate the spatial distribution of the fertilizer on the ground based on the measurement of initial flight conditions of fertilizer granules after their ejection by the spreading disk. The techniques developed are based on the analysis of images of the area around the disk showing the granule ejection. A high resolution - low cost imaging system for the analysis of high speed particle projection developed for this specific purpose is presented in this paper. The system, based …

[SDE] Environmental Sciences[SDE]Environmental Sciences
researchProduct

Jugement de la couleur des carcasses en abattoir. Eléments d’objectivation de la notation experte de la couleur des viandes de gros bovins Charolais

2016

Evaluation of carcass color at the slaughterhouse Expert notation is the reference method for the evaluation of bovine meat color, notably for quality. A total of 140 Charolais carcasses were scored at the slaughterhouse on a color scale going from 1 (too light) to 5 (too dark). These expert scores were compared to three types of instrumental data (L*a*b / RVB referential; image shooting with image processing / chromameter). Amongst the instrumental measurements, the values obtained after image processing of the whole muscle seems to be better associated with the scores obtained for color by experts than the scores obtained with the chromameter for which the zone of the muscle analyzed is m…

Grille de notation[SDV.SA.SPA]Life Sciences [q-bio]/Agricultural sciences/Animal production studiesChromamètre[SDV.SA.SPA] Life Sciences [q-bio]/Agricultural sciences/Animal production studiesCouleur de la viandeÉvaluation sensorielle de la couleurRéférentiel de couleur
researchProduct

Leaf surface roughness characterization by image processing

2013

International audience

[SDV] Life Sciences [q-bio][SDE] Environmental Sciencesleaf roughnessprecision agriculturecharacterization of the leaf surface[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal BiologyComputingMilieux_MISCELLANEOUSaccurate sprayingtexture analysisspectral analysis
researchProduct

Vine leaf roughness estimation by image processing

2013

International audience; The application of plant protection product has an important role in agricultural production processes. With current pesticides management, a huge amount of them are applied to worldwide orchards. In precision spraying, spray application efficiency depends on the pesticide application method, the phytosanitary product as well as the leaf surface properties. For environmental and economic reasons, the global trend is to reduce the pesticide application rate of the few approved active substances. Under these constraints, one of the challenges is to improve the efficiency of pesticide application. Different parameters can influence pesticide application such as nozzle t…

Leaf surface roughness[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SDE.IE]Environmental Sciences/Environmental EngineeringKernel Discriminant AnalysisNeural Network.Neural Network[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[ SDE.IE ] Environmental Sciences/Environmental EngineeringGeneralized Fourier Descriptor[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[SDE.IE] Environmental Sciences/Environmental EngineeringTexture[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
researchProduct

A 3-D simulation with virtual stereo rig for centrifugal fertilizer spreading

2012

International audience; 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…

[SDV] Life Sciences [q-bio][SDE] Environmental Sciencesfertilizer centrifugal spreader[SDV]Life Sciences [q-bio][SDE]Environmental SciencesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal BiologyGeneralLiterature_MISCELLANEOUSstereovisionComputingMethodologies_COMPUTERGRAPHICSimage processing
researchProduct

Variable rate fertilisation: Measurement of fertilizer granules motion on a centrifugal spreader by image analysis

2001

Although mechanically simple, centrifugal spreaders used for mineral fertilization involve complexphysics that cannot be fully characterized at the present time. We are developing sensors to evaluatethe spatial distribution of the fertilizer on the ground based on the measurement of initial flightconditions of fertilizer granules after their ejection by the spreading disk. The techniques developedare based on the analysis of images of the area around the disk showing the granule ejection. A fastimaging technique developed for this specific purpose is presented in this paper. It allows toautomatically compute the direction of ejection and velocity of each granule observed in the image.

[SDE] Environmental Sciences[SDE]Environmental Sciences
researchProduct

Mesure de netteté basée sur les descripteurs généralisés de Fourier appliquée à la reconstruction 3D par Shape from Focus

2013

National audience; L'étape principale de la méthode de reconstruction 3D " Shape from Focus " est l'utilisation d'un opérateur de mesure de netteté de chaque pixel de la séquence d'image. Le choix de l'opérateur de mesure de netteté est une étape cruciale pour une reconstruction 3D de qualité. La précision de la mesure de netteté dépend de la taille du voisinage autour du pixel choisi et de la présence ou non de bruit additif dans la séquence d'images. Dans cet article, nous présentons deux nouveaux opérateurs de mesure de netteté basés sur les Descripteurs Généralisés de Fourier. Une nouvelle étude comparative des différents opérateurs est présentée. Cette comparaison est basée sur un plan…

[SDE] Environmental Sciences[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SDV]Life Sciences [q-bio][ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing??[SDV] Life Sciences [q-bio]Mesure de netteté[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingDescripteurs généralisés de Fourier[SDE]Environmental SciencesShape from Focus[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biology[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
researchProduct

Centrifugal spreading : motion blurred images to determine the three components of fertiliser outlet velocity

2006

In the optical control of centrifugal spreading, the use of a single CCD camera to measure the outlet velocity is not limited to flat discs and 2D motion estimation. Combining the kinetic study of the fertiliser motion on the vane and the geometric analysis of image acquisition, the 3D components of the outlet velocity can be deduced from motion blurred images in the case of a traditional concave disc. The estimation of the horizontal and vertical outlet velocities is useful to predict spread patterns using ballistic flight models. This opens up the possibility to implement simple spreader test tools for quality diagnoses or sensors for feedback loop adjustments.

ANALYSE D'IMAGE[SDE] Environmental Sciences[SDE]Environmental Sciences
researchProduct

Autonomous phenotyping using a mobile manipulator

2018

International audience

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal BiologyComputingMilieux_MISCELLANEOUS
researchProduct

European and French level of study correspondence in ABE domain

2016

Biosystems Engineering (BE) term is a reference one in the USA and the UE, with sometimes very different definitions, used since more than a decade. Its contours and contents, for L and M degrees, have been studied in a European program for standardization inside the ERABEE network, allowing to designate this domain as a field of engineering which integrates engineering science and design with applied biological, environmental and agricultural sciences. Therefore, Biosystems Engineering is 'the branch of engineering that applies engineering sciences to solve problems involving biological systems ». This definition appeals to many skills and it is useful to mention that in France, the L1 and…

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biology
researchProduct

Détermination de la texture de la feuille de vigne par imagerie

2013

National audience; Dans le contexte de la pulvérisation de précision, nombreuses sont les recherches menées sur l'optimisation d'utilisation des produits phytosanitaires. L'objectif final étant de réduire de manière significative la quantité d'intrant dans les cultures . Dans ce cadre, les travaux présentés dans cet article s'intéresse particulièrement à l'analyse de l'état de surface foliaire qui présente une part essentielle dans le processus d'adhésion du produit pulvérisé sur la feuille. L'analyse de surface de la feuille est réalisée à travers l'analyse des caractéristiques texturale extraites d'images microscopics. Afin de discriminer les différents cépages et âges des feuilles retenu…

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Analyse discriminante linéaire et non linéaire[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]TextureDescripteur Généralise de FourierRéseau de neuronessurface foliaire[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
researchProduct

Vineyard leaf roughness characterization by computer vision and cloud computing technics

2010

International audience; In the context of vineyard leaf roughness analysis for precision spraying applications, this article deals with its characterization by computer vision and cloud computing techniques. The techniques merge feature extraction, linear or nonlinear dimensionality reduction techniques and several kinds of classification methods. Different combinations are processed and their performances compared in terms of classification error rate, in order to find the best association. However these combinations are hardly processed because of the lack of computing power and the prohibitive time consumption of the algorithms. To overcome these difficulties, we propose a solution: the …

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SDE.ES] Environmental Sciences/Environmental and Society[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SDE.ES]Environmental Sciences/Environmental and Society[ SDE.ES ] Environmental Sciences/Environmental and Society[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
researchProduct

A new tool for assessment of biostimulants effects on grapevine

2018

National audience; Despite an increasing interest for the use of biostimulants (BS) in agriculture, methods allowing a precise description of their effects on plants remain rather limited. In the IRIS+ FUI project, two major and highly different worldwide crops, wheat (annual, monocotyledon) and grapevine (perennial, broadleaf) were chosen to deepen our knowledge of such compounds and explore their potential additional interest. One objective was to develop a tool adapted to the screening and study of the impact of a series of BS on the development and physiology of these crops in controlled conditions. We managed to develop such a tool adapted to grapevine herbaceous cuttings. It allows a …

[SDV] Life Sciences [q-bio][SDE] Environmental Sciencesbiostimulantsphenotyping[SDV]Life Sciences [q-bio][SDE]Environmental Sciencesfood and beverages[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologygrapevine
researchProduct

Graph-based denoising of skeletonized root-systems

2015

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyroot systemgraphskeletonization
researchProduct

Special Issue "Remote Sensing and Proximal Sensing in Support of Agricultural Cultivation and Crop Risk Management"

2019

prod 2019-108 équipe BAP équipe EA GEAPSI AGROSUP

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biology
researchProduct

Motion coordination of a mobile manipulator within control framework: application to phenotyping

2018

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biology
researchProduct

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

2021

International audience; Special 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 …

[SPI]Engineering Sciences [physics]remote sensing[SPI] Engineering Sciences [physics]multi- and yperspectral data and sensorsprecision agriculture and viticultureimage acquisitionproximal sensingimage processing
researchProduct

Predicting spread patterns of centrifugal fertiliser spreaders

2014

International audience; Nowadays farmers recognize the importance of a correct and precise fertiliser application: non-uniform spread patterns cause extra pressure on the environment and might result in economic losses for the farmer. In Europe most spreading is done by centrifugal fertilizer spreaders but their spreading process is not easy to monitor and to control. To perform a precise fertilising farmers need proper tools to determine and evaluate the spread pattern at farm level. Therefore the Flemish Institute for Agricultural and Fisheries Research (ILVO) is exploring and developing a fast and accurate technique for measuring the spread pattern of conventional centrifugal spreaders. …

[SDE] Environmental Sciences[SDV]Life Sciences [q-bio]spread patternimage techniques[INFO.INFO-ES] Computer Science [cs]/Embedded Systemsballistic flight;fertiliser[SDV] Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biologycross correlation[SDV.BV] Life Sciences [q-bio]/Vegetal Biology[INFO.INFO-ES]Computer Science [cs]/Embedded Systems[ INFO.INFO-ES ] Computer Science [cs]/Embedded SystemsComputingMilieux_MISCELLANEOUS
researchProduct

Feasibility of selecting optimal textural features to detect foliar symptoms of ‘flavescence doree’ grapevine disease

2018

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biology
researchProduct

3D ACQUISITION SYSTEM APPLIED TO AGRONOMIC SCENES

2012

International audience; To improve results in automatic wheat ear counting by proxy-detection for early yield prediction, we need depth information of the scene. In this paper, we describe our 3D acquisition system dedicated to reconstruction of agronomic scenes. This system is composed of a camera mounted on a linear displacement driven by a microcontroller. The linear displacement allows acquiring a set of images in different distances to the scene. This image stack is used to apply shape from focus technique which is a passive and monocular 3D reconstruction method. This technique consists in the application of a focus measure for every pixel in the stack. An approximation method is used…

[SDE] Environmental Sciences[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SDV]Life Sciences [q-bio][SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/AgronomyComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONagronomic scenes[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingcrop analysis[SDV] Life Sciences [q-bio][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ SDV.SA.AGRO ] Life Sciences [q-bio]/Agricultural sciences/Agronomyacquisition system[SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biology3D reconstruction[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingComputingMethodologies_COMPUTERGRAPHICS
researchProduct

High resolution imagery for phenotypic root traits detection

2016

AgrosupINRAEAGEAPSI; High resolution imagery for phenotypic root traits detection. CIRG-AgEng 2016, International Conférence on Agricultural Engineering, Automation, Environment and Food Safety

[SDV] Life Sciences [q-bio][ SDV ] Life Sciences [q-bio][SDV]Life Sciences [q-bio]
researchProduct

Spray droplet characteristics measured using high speed imaging techniques

2016

Presented at Conference International Advances in Pesticide Application (IAPA), Barcelone, ESP (2016-01-13 - 2016-01-15).; International audience; Spray droplet characteristics are important features of an agricultural spray. The objective of this study is to measure the droplet size for different types of hydraulic spray nozzles using a developed backlighted image acquisition system and image processing technique. An in-focus droplet criterion was established to decide whether a droplet is in focus and can be measured in an accurate way. Tests included five different nozzles (Albuz ATR orange and red, TeeJet XR 110 01, XR 110 04 and Al 110 04).

[SDE] Environmental Sciences[SDV.SA]Life Sciences [q-bio]/Agricultural sciences[SDV.SA] Life Sciences [q-bio]/Agricultural sciences[SDV]Life Sciences [q-bio]droplet generatorspray characterisationdroplet size[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[SDV] Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyhigh speed imaging[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
researchProduct