References of "Destain, Marie-France"
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See detailULTRASONIC WAVES THROUGH AGRICULTURAL SOILS TO DETERMINE THEIR COMPACTION AND POROSITY LEVEL 
Luong, Jeanne ULg; Mercatoris, Benoît ULg; Destain, Marie-France ULg

Poster (2014)

Compaction is one of the major causes of the physical degradation of agricultural soils. The traffic of more and more heavy machines leads to a decrease of the porosity at both the topsoil and subsoil ... [more ▼]

Compaction is one of the major causes of the physical degradation of agricultural soils. The traffic of more and more heavy machines leads to a decrease of the porosity at both the topsoil and subsoil levels. This has negative impacts in agricultural and environmental contexts such as the reduction of soil fertility and water infiltration. This project aims at characterizing in a fast and non-destructive way the state of compaction of an agricultural soil at a local scale using ultrasonic wave propagation. Acoustic signatures of soil samples will be correlated to their compaction level and their porosity distribution. This should allow a better comprehension of the compaction process and help to define critical threshold. As a result, this methodology could assist in taking restrictive measures such as load limitation of agricultural engines and implementing remedial methods. This poster presents the experimental protocol implement for this research. [less ▲]

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See detailASSESSMENT OF PLANT LEAF AREA MEASUREMENT BY USING STEREO- VISION
Leemans, Vincent ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

in 2013 International Conference on 3D Imaging (IC3D) - Proceedings (2013, December)

The aim of this study is to develop an alternative measurement for the leaf area index (LAI), an important agronomic parameter for plant growth assessment. A 3D stereo-vision technique was developed to ... [more ▼]

The aim of this study is to develop an alternative measurement for the leaf area index (LAI), an important agronomic parameter for plant growth assessment. A 3D stereo-vision technique was developed to measure both leaf area and corresponding ground area. The leaf area was based on pixel related measurements while the ground area was based on the mean distance from the leaves to the camera. Laboratory and field experiments were undertaken to estimate the accuracy and the precision of the technique. Result showed that, though the leaves-camera distance had to be estimated precisely in order to have accurate measurement, the precision of the LAI evaluation, after regression, was equivalent to the reference measurements, that is to say around 10% of the estimated value. This shows the potential of the 3D measurements compared with tedious reference measurements. [less ▲]

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See detailA comparison of within-season yield prediction methodologies
Dumont, Benjamin ULg; Basso, Bruno; Bodson, Bernard ULg et al

Conference (2013, November)

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See detailLeaf area and leaf orientation measurement by using stereo-vision
Leemans, Vincent ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

Conference (2013, September 03)

This paper presents a 3D stereo-vision system aiming at the measurement of plant characteristics. The method is intended to be used in an Ecotron where the availability of plant material is limited and ... [more ▼]

This paper presents a 3D stereo-vision system aiming at the measurement of plant characteristics. The method is intended to be used in an Ecotron where the availability of plant material is limited and where crop should be characterised non destructively. The plants were not considered as individuals, it was the crop as a whole that was characterised. The leaf area index (LAI) was measured. It is an important property of vegetation, since it determines the photosynthetic primary production, the plant evaporation and characterises the plant growth. The average leaf angle (ALA) was also measured. For the computation, leaf pixels were differentiated from soil pixels by using linear discriminant analysis. The stereo vision system computed the distance to the camera of each pixel in the image in the region where the pixels are present in both images. The observed area was computed on the basis of the average distance of the leaf pixels in the region. The leaf area was evaluated for each triplet of adjacent pixels by computing the cross product of the vectors defined by those three points. The sum gave the leaf area for the same region. The area of these triangles was summed for all the pixels in the region and the ratio to the observed area gave the LAI. The ALA was the mean orientation of the pixel triplets. After calibration, the method was found to present a coefficient of correlation of 0.93 with destructive reference measurements and a precision of 0.12 for the LAI. It was possible to measure the LAI and the ALA from the germination up to the ripening stage with a minimum of work load. [less ▲]

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See detailYield variability linked to climate uncertainty and nitrogen fertilisation
Dumont, Benjamin ULg; Basso, Bruno; Leemans, Vincent ULg et al

in Stafford, John V. (Ed.) Precision agriculture '13 (2013, July)

At the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilisation). In combination with a weather ... [more ▼]

At the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilisation). In combination with a weather generator, we built up a general methodology that allows studying the yield variability linked to climate uncertainty, in order to assess the best N practice. Our study highlighted that, applying the Belgian farmer current N practice (60 60 60 kgN.ha-1), the yield distribution was found to be very asymmetric with a skewness of -1.02 and a difference of 5% between the mean (10.5 t.ha-1) and the median (11.05 t.ha-1) of the distribution. Which implied that, under such practice, the probability for farmers to achieve decent yields, in comparison of the mean of the distribution, was the highest. [less ▲]

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See detailPrediction of non-linear time-variant dynamic crop model using bayesian methods
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

in John Stafford (Ed.) Precision agriculture '13 (2013, July)

This work addresses the problem of predicting a non-linear time-variant leaf area index and soil moisture model (LSM) using state estimation. These techniques include the extended Kalman filter (EKF ... [more ▼]

This work addresses the problem of predicting a non-linear time-variant leaf area index and soil moisture model (LSM) using state estimation. These techniques include the extended Kalman filter (EKF), particle filter (PF) and the more recently developed technique, variational filter (VF). In the comparative study, the state variables (the leaf-area index LAI, the volumetric water content of the layer 1, HUR1 and the volumetric water content of the layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error with respect to the noise-free data. The results show that VF provides a significant improvement over EKF and PF. [less ▲]

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See detailA Site-Specific Grain Yield Response Surface : Computing the Identity Card of a Crop Under Different Nitrogen Management Scenarios
Dumont, Benjamin ULg; Basso, Bruno; Leemans, Vincent ULg et al

in The acts of the EFITA2013 congress (2013, June)

At the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilization). In combination with a weather ... [more ▼]

At the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilization). In combination with a weather generator, we propose a general methodology that allows studying the yield variability linked to climate uncertainty, in order to assess the best practices in applying fertilizers. Our study highlights that, using the usual practice of Belgian farmers, namely applying three doses of 60kgN/ha, the yield’s distribution presents the highest degree of asymmetry. This implies the highest probability to achieve yields superior to the mean. The computed return time of expected yield shows that 9 years out of 10, a grain yield of 7.26 tons.ha-1 could at least be achieved. [less ▲]

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See detailZoom sur la rétention par les plantes
Lebeau, Frédéric ULg; Massinon, Mathieu ULg; Destain, Marie-France ULg

Conference given outside the academic context (2013)

This video aims to get an insight on the mechanisms involved in retention of pesticides on plants. Using high magnification lenses, high speed camera and led back-light, the elaboration of retention on ... [more ▼]

This video aims to get an insight on the mechanisms involved in retention of pesticides on plants. Using high magnification lenses, high speed camera and led back-light, the elaboration of retention on plant leaves is better understood. The behavior of different drops diameters and speed is observed and linked to the physics behind. The video is dedicated to plant protection products users and should give them a clear understanding of the relevant parameters to be mastered to avoid losses and reduce polution. [less ▲]

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See detailModeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

in Masegosa, Antoçnio; Villacorta, Pablo; Cruz-Corona, Carlos (Eds.) et al Exploring Innovative and Successful Applications of Soft Computing (2013)

The problem of state/parameter estimation represents a key issue in crop models which are nonlinear, non-Gaussian and include a large number of parameters. The prediction errors are often important due to ... [more ▼]

The problem of state/parameter estimation represents a key issue in crop models which are nonlinear, non-Gaussian and include a large number of parameters. The prediction errors are often important due to uncertainties in the equations, the input variables, and the parameters. The measurements needed to run the model (input data), to perform calibration and validation are sometimes not numerous or known with some uncertainty. In these cases, estimating the state variables and/or parameters from easily obtained measurements can be extremely useful. In this work, we address the problem of modeling and prediction of leaf area index and soil moisture (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and the more recently developed technique variational Bayesian filter (VF). The objective of this work is to extend the state and parameter estimation techniques (i.e., EKF, UKF, PF and VF) to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The randomness of the expectation and the covariance of the state/parameter vector are assumed here to further capture the uncertainty of the state distribution. One practical choice of these distributions can be a Gaussian distribution for the expectation and a multi-dimensional Wishart distribution for the covariance matrix. The assumption of random mean and random covariance of the state leads to a probability distribution covering a wide range of tail behaviors, which allows discrete jumps in the state variables. [less ▲]

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See detailModeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

in Masegosa, Antoçnio; Villacorta, Pablo; Cruz-Corona, Carlos (Eds.) et al Exploring Innovative and Successful Applications of Soft Computing (2013)

The problem of state/parameter estimation represents a key issue in crop models which are nonlinear, non-Gaussian and include a large number of parameters. The prediction errors are often important due to ... [more ▼]

The problem of state/parameter estimation represents a key issue in crop models which are nonlinear, non-Gaussian and include a large number of parameters. The prediction errors are often important due to uncertainties in the equations, the input variables, and the parameters. The measurements needed to run the model (input data), to perform calibration and validation are sometimes not numerous or known with some uncertainty. In these cases, estimating the state variables and/or parameters from easily obtained measurements can be extremely useful. In this work, we address the problem of modeling and prediction of leaf area index and soil moisture (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and the more recently developed technique variational Bayesian filter (VF). The objective of this work is to extend the state and parameter estimation techniques (i.e., EKF, UKF, PF and VF) to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The randomness of the expectation and the covariance of the state/parameter vector are assumed here to further capture the uncertainty of the state distribution. One practical choice of these distributions can be a Gaussian distribution for the expectation and a multi-dimensional Wishart distribution for the covariance matrix. The assumption of random mean and random covariance of the state leads to a probability distribution covering a wide range of tail behaviors, which allows discrete jumps in the state variables. [less ▲]

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See detailModeling and Prediction of Nonlinear Environmental System Using Bayesian Methods
Mansouri, Majdi; Dumont, Benjamin ULg; Destain, Marie-France ULg

in Computers & Electronics in Agriculture (2013), 92

An environmental dynamic system is usually modeled as a nonlinear system described by a set of nonlinear ODEs. A central challenge in computational modeling of environmental systems is the determination ... [more ▼]

An environmental dynamic system is usually modeled as a nonlinear system described by a set of nonlinear ODEs. A central challenge in computational modeling of environmental systems is the determination of the model parameters. In these cases, estimating these variables or parameters from other easily obtained measurements can be extremely useful. This work addresses the problem of monitoring and modeling a leaf area index and soil moisture model (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), the particle filter (PF), and the more recently developed technique variational filter (VF). Specifically, two comparative studies are performed. In the first comparative study, the state variables (the leaf-area index LAI , the volumetric water content of the soil layer 1, HUR1 and the volumetric water content of the soil layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of number of estimated model parameters on the accuracy and convergence of these techniques are also assessed. The results of both comparative studies show that the PF provides a higher accuracy than the EKF, which is due to the limited ability of the EKF to handle highly nonlinear processes. The results also show that the VF provides a significant improvement over the PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the VF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The results of the second comparative study show that, for all techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. However, the VF can still provide both convergence as well as accuracy related advantages over other estimation methods. [less ▲]

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See detailVers un système de prédiction du rendement en temps réel
Dumont, Benjamin ULg; Ferrandis Vallterra, Salvador ULg; Leemans, Vincent ULg et al

Poster (2012, October 16)

L'objectif de cette recherche est le développement d'un outil capable de prédire les rendements d'une culture de blé en temps réel, au fur et à mesure que la saison avance. Pour atteindre cet objectif ... [more ▼]

L'objectif de cette recherche est le développement d'un outil capable de prédire les rendements d'une culture de blé en temps réel, au fur et à mesure que la saison avance. Pour atteindre cet objectif, nous avons développé une méthodologie qui repose sur l'adjonction des éléments suivants : (i) le modèle de culture STICS (INRA, France), (ii) un réseau de capteurs sans fil pour le monitoring des variables agro-environnementales (éKo system, The Crossbow technologies, USA) et (iii) une base de données météorologiques. [less ▲]

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See detailIdentification of bacteria community associated with earthworm gut
Lemtiri, Aboulkacem ULg; Alabi, Taofic; Bodson, Bernard ULg et al

Poster (2012, July 26)

The role of earthworms in soil fertility and transformation of organic waste was regulary cited to be of first importance. Associated to these macro-invertebrates, a large diversity of micro-orgnisms are ... [more ▼]

The role of earthworms in soil fertility and transformation of organic waste was regulary cited to be of first importance. Associated to these macro-invertebrates, a large diversity of micro-orgnisms are found indirectly in their closed environment or directly in their gut. Functional aspects of these interactions and symbiosis in relation with soil characteristics and fertility rates are poorly developed. Here, the micro-organisms diversity and potential related functions of earthworm gut were investigated using a proteiomic approach for both protein and micro-organism identifications. Microbial community investigation was detected by proteomic approach based on bidimensional electrophoresis coupled with mass spectrometry using Matrix Assisted Laser Desorption Ionisation – time of flight (Maldi-Tof). Diversity of gut associated bacterial communities was discussed. Indeed, application of particular crop production practices such as crop residue management at the field level could regulate the gut bacterial communities in earthworm but also microbials in soils. Agricultural systems had to consider the microbial and associated organisms in the soil to enhance fertlility and crop production in sustainable ways. [less ▲]

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See detailAssessment of pesticide application method efficiency by high-speed image analysis
Massinon, Mathieu ULg; Denis, Thierry; Perriot, Benjamin et al

Conference (2012, July 08)

This paper investigates if increased blackgrass weeding efficiency by reduced volume per hectare observed during 2010 Arvalis field trials may be related to increased pesticide application method ... [more ▼]

This paper investigates if increased blackgrass weeding efficiency by reduced volume per hectare observed during 2010 Arvalis field trials may be related to increased pesticide application method efficiency. Retention on blackgrass leaves was assessed by an image analysis method. The setup consists of a high-speed camera shooting drop impact on horizontal leaf target. An herbicide (Archipel® [125 g/ha] + Actirob® [1 l/ha]) was sprayed at the usual volume of 150 l/ha and at a reduced volume of 65 l/ha. Adjuvants use (Epsotop® [1%] + Heliosol® [0.5%]) was also evaluated at 65 l/ha to highlight the effect of mixture surface tension modification. Drop properties before impact were extracted by image analysis and a phase diagram derived. Volumetric proportions of impact types are determined inside 11 energy classes to assess the effect of formulation and application method. The volume median diameter (VMD) before impact was slightly decreased by the reduction to 65 l/ha because of nozzle and pressure changes and also by the use of the adjuvants leading to the reduction of surface tension. Without adjuvants the reduction to 65l/ha increased the proportion of adhesion while rebound remained unchanged and fragmentation decreased. With adjuvants, drop fragmentation occurs for a lower energy class but the proportion of fragmentation also decreases with because of reduced VMD. A slight effect on the transitions between impact classes was observed because of formulation concentration change at reduced volume/hectare. A major effect of adjuvants on retention was highlighted as bouncing disappeared. [less ▲]

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See detailImpact of soil management on earthworm diversity according to differential plowing and plant residue incorporation
Lemtiri, Aboulkacem ULg; Alabi, Taofic; Zirbes, Lara ULg et al

Poster (2012, July 02)

Earthworms are largely distributed in terrestrial ecosystems and their abundance and diversity in soils are significantly affected by biotic (macro- and micro-organisms) and abiotic factors: soil ... [more ▼]

Earthworms are largely distributed in terrestrial ecosystems and their abundance and diversity in soils are significantly affected by biotic (macro- and micro-organisms) and abiotic factors: soil properties (pH, texture, structure…); agricultural management system and climate change. Here, tillage effect of earthworm population combined with crops residual management was investigated and correlated with soils properties. From wheat experimental field plots, the diversity of earthworm according to the field crop management was assessed. Application of particular crop production practices such as the integration of different levels of crop residues, diverse parts of wheat straws, at the field level regulate earthworm diversity and population abundance. Indeed, tillage reduced earthworm population with a 35% rate also corresponding to changes in soil properties. Agricultural practices had to be adapted to include consideration on macro-invertebrate abundance and diversity to maintain efficient soil fertility and allow sustainable crop production [less ▲]

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See detailModeling of the Diffusion of VOCs Emitted by Barley Roots
Hirtt, Laura ULg; Destain, Marie-France ULg; Lognay, Georges ULg et al

Poster (2012, July 02)

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See detailAssessing the potential of an algorithm based on mean climatic data to predict wheat yield.
Dumont, Benjamin ULg; Leemans, Vincent ULg; Ferrandis Vallterra, Salvador ULg et al

in Proceedings of the 11th International Conference on Precision Agriculture (2012, July)

Real-time non-invasive determination of crop biomass and yield prediction are maybe among the major challenges in agriculture. But unknown future weather remains the key point of accurate yield forecast ... [more ▼]

Real-time non-invasive determination of crop biomass and yield prediction are maybe among the major challenges in agriculture. But unknown future weather remains the key point of accurate yield forecast. This paper presents the results of a preliminary study that aims to supply the unknown future by daily mean climatic data. The results show that under the Belgian weather, this approach is relevant. Furthermore, the developed methodology appears to be a powerful diagnosis tool of the remaining yield potential under ongoing weather. [less ▲]

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