References of "Leemans, Vincent"
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See detailParameter identification of the STICS crop model, using an accelerated formal MCMC approach
Dumont, Benjamin ULg; Leemans, Vincent ULg; Mansouri, Majdi ULg et al

in Environmental Modelling & Software (2014), 52

This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The ... [more ▼]

This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L.) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling [less ▲]

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See detailBayesian methods for predicting LAI and soil water content
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Leemans, Vincent ULg et al

in Precision Agriculture (2014), 15(2), 184-201

LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was ... [more ▼]

LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: Extended Kalman Filtering (EKF), Particle Filtering (PF), and Variational Filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error (RMSE) of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation. [less ▲]

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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, Salvador et al

in Precision Agriculture (2014)

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in ... [more ▼]

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days. [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 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 detailGame species monitoring using road–based distance sampling in association with thermal imagers: a covariate analysis
Morelle, Kevin ULg; Bouché, Philippe; Lehaire, François ULg et al

in Animal Biodiversity and Conservation (2012), 35(2), 253-265

Monitoring of game species populations is necessary to adequately assess culling by hunters in areas where natural large predators are absent. However, game managers have to control several species and ... [more ▼]

Monitoring of game species populations is necessary to adequately assess culling by hunters in areas where natural large predators are absent. However, game managers have to control several species and they often lack of an efficient and convenient survey design method. Monitoring several species at that same time over large areas could thus be cost– and time–effective. We tested the influence of several factors during monitoring of three common game species, (wild boar, roe deer and red fox, using road–based distance sampling in association with thermal imagers. This pilot survey based on 20 night counts in five contrasting sites studied the effect of several covariates (species, thermal imaging, observer, group size, and habitat type) on the detection probabilities. No differences were observed between thermal imagers and group sizes , but we found differences between observers . Expected differences were also observed between species and between habitat type. Our results show that the detectability of low cost thermal imaging equipment is similar to that of more expensive methods, highlighting new possibilities for the use of thermal imagery by game managers. Although adjustments should be made to the study design our findings suggest that large–scale multi–species monitoring could be an efficient method for common game species. [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 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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See detailA method for plant leaf area measurement by using stereo vision
Leemans, Vincent ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg et al

in Proceedings of CIGR-AgEng 2012 International Conference on Agricultural Engineering (2012, July)

This paper presents a method for the measurement of LAI of wheat in situ. By using stereoscopic images a 3D map was computed. One colour image was segmented to identify plant regions and the 3D leaf area ... [more ▼]

This paper presents a method for the measurement of LAI of wheat in situ. By using stereoscopic images a 3D map was computed. One colour image was segmented to identify plant regions and the 3D leaf area was computed on these regions. The result showed that the precision was about the same as for the reference measurements but required a lesser workload. [less ▲]

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See detailA first step towards a real-time predictive yield support system.
Dumont, Benjamin ULg; Leemans, Vincent ULg; Ferrandis Vallterra, Salvador ULg et al

in Agriculture & Engineering for a Healthier Life (2012)

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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See detailAutomatic grading of Bi-colored apples by multispectral machine vision
Unay, Devrim; Gosselin, Bernard; Kleynen, Olivier et al

in Computers & Electronics in Agriculture (2011)

In this paper we present a novel application work for grading of apple fruits by machine vision. Following precise segmentation of defects by minimal confusion with stem/calyx areas on multispectral ... [more ▼]

In this paper we present a novel application work for grading of apple fruits by machine vision. Following precise segmentation of defects by minimal confusion with stem/calyx areas on multispectral images, statistical, textural and geometric features are extracted from the segmented area. Using these features, statistical and syntactical classifiers are trained for two- and multi-category grading of the fruits. Results showed that feature selection provided improved performance by retaining only the important features, and statistical classifiers outperformed their syntactical counterparts. Compared to the state-of-the-art, our two-category grading solution achieved better recognition rates (93.5% overall accuracy). In this work we further provided a more realistic multi-category grading solution, where different classification architectures are evaluated. Our observations showed that the single-classifier architecture is computationally less demanding, while the cascaded one is more accurate. [less ▲]

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See detailEvaluation of the performance of infrared thermography for on-line condition monitoring of rotating machines
Leemans, Vincent ULg; Destain, Marie-France ULg; Kilundu, Bovic et al

in Engineering (2011), (3), 1030-1039

This study evaluated the possibility of infrared thermography to measure accurately the temperature of elements of a rotating device, within the scope of condition monitoring. The tested machine was a ... [more ▼]

This study evaluated the possibility of infrared thermography to measure accurately the temperature of elements of a rotating device, within the scope of condition monitoring. The tested machine was a blower coupled to a 500 kW electric motor, that operated in multiples regimes. The thermograms were acquired by a fixed thermographic camera and were processed and recorded every 15 minutes. Because the normal temperature variations could easily mask a drift caused by a failure, a corrected temperature was computed using autorecursive models. It was shown that an efficient temperature correction should compensate for the variations of the process, and for the ambient temperatures variations, either daily or seasonal. The standard deviation of the corrected temperature was of a few tenth of degree, making possible the detection of a drift of less than one degree and the prediction of potential failure. [less ▲]

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See detailREAL-TIME MONITORING OF ENVIRONMENTAL FACTORS TO MODEL WHEAT YIELD PRODUCTION
Dumont, Benjamin ULg; Leemans, Vincent ULg; Lebeau, Frédéric ULg et al

Poster (2010, September 07)

This paper presents the results of a one year preliminary study in which a real-time monitoring system was used to feed the STICS soil crop model. The monitoring system was made of a self-organising ... [more ▼]

This paper presents the results of a one year preliminary study in which a real-time monitoring system was used to feed the STICS soil crop model. The monitoring system was made of a self-organising wireless network within which microsensors collected and stored microclimatic and environmental data. As indicated by the statistical criteria (RMSE, normalized deviation and model efficiency), the optimisation of some wheat crop parameters allows the STICS model to predict the yields with good accuracy for three different soil types and seven different nitrogen application rates. [less ▲]

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See detailA sorting optimization curve with quality and yield requirements
Ooms, David ULg; Palm, Rodolphe ULg; Leemans, Vincent ULg et al

in Pattern Recognition Letters (2010), 31(9), 983-990

Binary classifiers used for sorting can be compared and optimized using receiver-operating characteristic (ROC) curves which describe the trade-off between the false positive rate and true positive rate ... [more ▼]

Binary classifiers used for sorting can be compared and optimized using receiver-operating characteristic (ROC) curves which describe the trade-off between the false positive rate and true positive rate of the classifiers. This approach is well suited for the diagnosis of human diseases where individual costs of misclassification are of great concern. While it can be applied to the sorting of merchandise or other materials, the variables described by the ROC curve and its existing alternatives are less relevant for that range of applications and another approach is needed. In this paper, quality and yield factors are introduced into a sorting optimization curve (SOC) for the choice of the operating point of the classifier, associated with the prediction of output quantity and quality. Given examples are the sorting of seeds and apples with specific requirements. In both cases the operating point of the classifier is easily chosen on the SOC, while the output characteristics of the sorted product are accurately predicted. [less ▲]

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See detailSurveillance en ligne d'une machine tournante par thermographie infrarouge
Leemans, Vincent ULg; Destain, Marie-France ULg

Conference (2009, December 07)

Des mesures par thermographie d'une machine tournante critique (ventilateur) sont réalisées en ligne pour évaluer la température d’organes, comme le moteur et les paliers. En conditions de fonctionnement ... [more ▼]

Des mesures par thermographie d'une machine tournante critique (ventilateur) sont réalisées en ligne pour évaluer la température d’organes, comme le moteur et les paliers. En conditions de fonctionnement normal, les températures montrent des variations importantes, caractérisées par un écart­-type de l’ordre de 5 °C. Elles sont liées aux variations de conditions climatiques et à l'apport de chaleur variable en fonction du processus de fabrication. Un modèle auto­récurrent avec grandeurs d'entrée (ARX) a permis d'éliminer ces variations. Le bruit sur la température corrigée est réduit à un degré Celsius, ce qui permet de mettre en évidence très tôt une dégradation au niveau des paliers principaux. Par contre, dans le cas présent, le moteur subit un encrassement et donc une augmentation de température en cours de fonctionnement qui masque l’existence d’une éventuelle détérioration. [less ▲]

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See detailImproving in-row weed detection in multispectral stereoscopic images
Piron, Alexis ULg; Leemans, Vincent ULg; Lebeau, Frédéric ULg et al

in Computers & Electronics in Agriculture (2009), 69

Previous research has shown that plant height and spectral reflectance are relevant features to classify crop and weeds in organic carrots: classification based on height gave a classification accuracy ... [more ▼]

Previous research has shown that plant height and spectral reflectance are relevant features to classify crop and weeds in organic carrots: classification based on height gave a classification accuracy (CA) of up to 83% while classification based on a combination of three multispectral bands gave a CA of 72%. The first goal of this study was to examine the simultaneous use of both height and multispectral parameters. It was found that classification rate was only slightly improved when using a feature set comprising both height and multispectral data (2%). The second goal of this study was to improve the detection method based on plant height by setting an automatic threshold between crop and weeds heights, in their early growth stage. This threshold was based on crop row determination and peak detection in plant height probability density function, corresponding to the homogeneous crop population. Using this method, the CA was 82% while the CA obtained with optimal plant height limits is only slightly higher at 86%. [less ▲]

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See detailUltrasonic internal defect detection in cheese
Leemans, Vincent ULg; Destain, Marie-France ULg

in Journal of Food Engineering (2009), 90(3), 333-340

Different ultrasonic signals and detection techniques were used and compared to detect internal foreign bodies present in semi-soft cheeses. The signals were a pulse or a chirp and the detection was ... [more ▼]

Different ultrasonic signals and detection techniques were used and compared to detect internal foreign bodies present in semi-soft cheeses. The signals were a pulse or a chirp and the detection was carried out by using either correlation with a reference signal or a wavelet decomposition. The principle of the detection consisted in measuring the time of flight of the transmitted signals and of the echoes, the latter in the absence of foreign body should be the double of the former. The presence of a foreign object affected this pattern in several ways. In order to assess the method, a small plastic cylindrical object of 3 mm in diameter was introduced in one half of the cheese and was tested for detection, the second half being used as reference for the control cheese. The results showed that the two signals and the two detection methods were able to localise the transmitted signals and the echo from the opposite face of the cheese under all circumstances. For the foreign body detection, the correlation method gave superior results, in term of signal to noise ratio as well as in term of error rate, while the two signals gave similar results. The analysis of the mean and standard deviation of the signal to noise ratio of the object echo showed that some samples presented peak values close to those due to the noise. Nevertheless, the object was detected in 90% of the tests. There was no significant effect of temperature on the detection technique. (C) 2008 Elsevier Ltd. All rights reserved. [less ▲]

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See detailApplication de la Thermographie infrarouge au diagnostic
Leemans, Vincent ULg

in Les méthodes et outils de diagnostic et de contrôle appliqués pour la maintenance et la maîtrise des risques (2008, October 30)

Présentation de l'état de l'art en matière de maintenance, par thermographie infrarouge

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See detailSelection of the most efficient wavelength bands for discriminating weeds from crop
Piron, Alexis ULg; Leemans, Vincent ULg; Kleynen, O. et al

in Computers & Electronics in Agriculture (2008), 62(2), 141-148

The aim of this study was to select the best combination of filters for detecting various weed species located within carrot rows. In-field images were taken under artificial lighting with a multispectral ... [more ▼]

The aim of this study was to select the best combination of filters for detecting various weed species located within carrot rows. In-field images were taken under artificial lighting with a multispectral device consisting of a black and white camera coupled with a rotating wheel holding 22 interference filters in the VIS-NIR domain. Measurements were performed over a period of 19 days, starting 1 week after crop emergence (early weeding can increase yields) and seven different weeds species were considered. The selection of the best filter combination was based on a quadratic discriminant analysis. The best combination of filters included three interference filters, respectively centred on 450, 550 and 700 nm. With this combination, the overall classification accuracy (CA) was 72%. When using only two filters, a slight degradation of the CA was noticed. When the classification results were reported on field images, a systematic misclassification of carrot cotyledons appears. Better results were obtained with a more advanced growth stage. (c) 2007 Elsevier B.V. All rights reserved. [less ▲]

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