As We Are Blind

Œuvres et Recherches 2019:01« As We Are Blind » (2016) est une installation pour « piano mécanique et aura » de Véronique Béland. Au centre d’une pièce à la scénographie épurée, un piano à queue joue la partition la plus intime : celle du spectateur. Conductance, température de la peau, poids de la main, rythme cardiaque... Chaque participant pose la main sur un capteur, et « As We Are Blind » calcule et interprète une production musicale et photographique unique, qui révèle l’« aura » colorée de chaque spectateur

The Yin-Yang of the Green Fluorescent Protein:Impact on Saccharomyces cerevisiae stress resistance

International audienceAlthough fluorescent proteins are widely used as biomarkers (Yin), no study focuses on their influence on the microbial stress response. Here, the Green Fluorescent Protein (GFP) was fused to two proteins of interest in Saccharomyces cerevisiae. Pab1p and Sur7p, respectively involved in stress granules structure and in Can1 membrane domains. These were chosen since questions remain regarding the understanding of the behavior of S. cerevisiae facing different heat kinetics or oxidative stresses. The main results showed that Pab1p-GFP fluorescent mutant displayed a higher resistance than that of the wild type under a heat shock. Moreover, fluorescent mutants exposed to oxidative stresses displayed changes in the cultivability compared to the wild type strain. In silico approaches showed that the presence of the GFP did not influence the structure and so the functionality of the tagged proteins meaning that changes in yeast resistance were certainly related to GFP ROS-scavenging ability (Yang)

Detection of compact forms in imaging : Development of cumulative methods based on the study of gradients : Applications for the food

Les cellules de comptage (Malassez, Thoma …) sont conçues pour permettre le dénombrement de cellules sous microscope et la détermination de leur concentration grâce au volume calibré de la grille apparaissant dans l’image microscopique. Le comptage manuel présente des inconvénients majeurs : subjectivité, non-répétabilité… Il existe des solutions commerciales de comptage automatique dont l’inconvénient est de nécessiter un environnement bien contrôlé qu’il n’est pas possible d’obtenir dans le cadre de certaines études (ex. : le glycérol influe grandement sur la qualité des images). L’objectif du projet est donc double : un comptage des cellules automatisé et suffisamment robuste pour être réalisable, quelles que soient les conditions d’acquisition.Dans un premier temps, une méthode basée sur la transformée de Fourier a été développée pour détecter, caractériser et effacer la grille de la cellule de comptage. Les caractéristiques de la grille extraites par cette méthode servent à déterminer une zone d’intérêt et son effacement permet de faciliter la détection des cellules à compter.Pour réaliser le comptage, la problématique principale est d’obtenir une méthode de détection des cellules suffisamment robuste pour s’adapter aux conditions d’acquisition variables. Les méthodes basées sur les accumulations de gradients ont été améliorées par l’adjonction de structures permettant une détection plus fine des pics d’accumulation. La méthode proposée permet une détection précise des cellules et limite l’apparition de faux positifs.Les résultats obtenus montrent que la combinaison de ces 2 méthodes permet d’obtenir un comptage répétable et représentatif d’un consensus des comptages manuels réalisés par des opérateurs.The counting cells (Malassez, Thoma ...) are designed to allow the enumeration of cells under a microscope and the determination of their concentration thanks to the calibrated volume of the grid appearing in the microscopic image. Manual counting has major disadvantages: subjectivity, non-repeatability ... There are commercial automatic counting solutions, the disadvantage of which is that a well-controlled environment is required which can’t be obtained in certain studies ( eg glycerol greatly affects the quality of the images ). The objective of the project is therefore twofold: an automated cell count and sufficiently robust to be feasible regardless of the acquisition conditions.In a first step, a method based on the Fourier transform has been developed to detect, characterize and erase the grid of the counting cell. The characteristics of the grid extracted by this method serve to determine an area of interest and its erasure makes it easier to detect the cells to count.To perform the count, the main problem is to obtain a cell detection method robust enough to adapt to the variable acquisition conditions. The methods based on gradient accumulations have been improved by the addition of structures allowing a finer detection of accumulation peaks. The proposed method allows accurate detection of cells and limits the appearance of false positives.The results obtained show that the combination of these two methods makes it possible to obtain a repeatable and representative count of a consensus of manual counts made by operators

Détection de formes compactes en imagerie : développement de méthodes cumulatives basées sur l'étude des gradients : Applications à l'agroalimentaire

The counting cells (Malassez, Thoma ...) are designed to allow the enumeration of cells under a microscope and the determination of their concentration thanks to the calibrated volume of the grid appearing in the microscopic image. Manual counting has major disadvantages: subjectivity, non-repeatability ... There are commercial automatic counting solutions, the disadvantage of which is that a well-controlled environment is required which can’t be obtained in certain studies ( eg glycerol greatly affects the quality of the images ). The objective of the project is therefore twofold: an automated cell count and sufficiently robust to be feasible regardless of the acquisition conditions.In a first step, a method based on the Fourier transform has been developed to detect, characterize and erase the grid of the counting cell. The characteristics of the grid extracted by this method serve to determine an area of interest and its erasure makes it easier to detect the cells to count.To perform the count, the main problem is to obtain a cell detection method robust enough to adapt to the variable acquisition conditions. The methods based on gradient accumulations have been improved by the addition of structures allowing a finer detection of accumulation peaks. The proposed method allows accurate detection of cells and limits the appearance of false positives.The results obtained show that the combination of these two methods makes it possible to obtain a repeatable and representative count of a consensus of manual counts made by operators.Les cellules de comptage (Malassez, Thoma …) sont conçues pour permettre le dénombrement de cellules sous microscope et la détermination de leur concentration grâce au volume calibré de la grille apparaissant dans l’image microscopique. Le comptage manuel présente des inconvénients majeurs : subjectivité, non-répétabilité… Il existe des solutions commerciales de comptage automatique dont l’inconvénient est de nécessiter un environnement bien contrôlé qu’il n’est pas possible d’obtenir dans le cadre de certaines études (ex. : le glycérol influe grandement sur la qualité des images). L’objectif du projet est donc double : un comptage des cellules automatisé et suffisamment robuste pour être réalisable, quelles que soient les conditions d’acquisition.Dans un premier temps, une méthode basée sur la transformée de Fourier a été développée pour détecter, caractériser et effacer la grille de la cellule de comptage. Les caractéristiques de la grille extraites par cette méthode servent à déterminer une zone d’intérêt et son effacement permet de faciliter la détection des cellules à compter.Pour réaliser le comptage, la problématique principale est d’obtenir une méthode de détection des cellules suffisamment robuste pour s’adapter aux conditions d’acquisition variables. Les méthodes basées sur les accumulations de gradients ont été améliorées par l’adjonction de structures permettant une détection plus fine des pics d’accumulation. La méthode proposée permet une détection précise des cellules et limite l’apparition de faux positifs.Les résultats obtenus montrent que la combinaison de ces 2 méthodes permet d’obtenir un comptage répétable et représentatif d’un consensus des comptages manuels réalisés par des opérateurs

RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

International audienceIn precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development

Chapter 26. High throughput field phenotyping (HTFP) of wheat and weed cover in field experiments using RGB images: assessment of crop-weed competition with a simple ecophysiological model

International audienceThe early stages of growth for two winter wheat cultivars, Apache and Rubisko, were studied in field experiments based on destructive measurements and visible images. They cover the period from the three-leaf stage to tillering at four sampling dates. Maps of fractional vegetation cover (FVC) were established for both the crops and weeds. FVC was automatically determined from the images with an SVM-RBF classifier, using Bag of Visual Words vectors as inputs. The heterogeneity in populations and crop-weed competition were studied using descriptive and inferential statistics. The impact of weeds on crops was evaluated by comparing the results with simulations under unstressed conditions

Evaluation of weed impact on wheat biomass by combining visible imagery with a plant growth model: towards new non-destructive indicators for weed competition

International audienceTo evaluate the impact of weeds on crops, precise identification and early prediction are required. This paper presents two new non-destructive indicators deduced from visible images: weed pressure (WP) and wheat growth status (WGS). They are based on the fractional vegetation cover (FVC) obtained from digital vegetation maps (crop vs. weeds) in a wheat field. FVC was determined for both plants with a Matthews Correlation Coefficient of 0.86 using machine learning classification [support vector machine-radial basis function (SVM-RBF)] combined with Bag of Visual Words technique. It was compared to destructive measurements of above-ground biomass (BM) and leaf area index (LAI). Since the coefficient of determination between FVC and BM is very good for wheat crop (r2 = 0.93), FVC is used to feed a growth model based on the Monteith equation. Replacing the standard approach by the image approach in the initialization of the model had no impact on the simulated BM values. WP characterized weed pressure, namely the FVCw/FVCc ratio and it quantified the crop–weed competition. The results show that up to the tillering stage, it could substitute for the BMw/BMc ratio resulting from a destructive approach. The second indicator, WGS assessed crop health through the monitoring of biomass production. It compared the theoretical wheat biomass simulated under non-stressed conditions, BMsimulated, to the actual biomass, BMobserved. The impact of weed on crop was evaluated by combining the results of these two indicators. This simple and fast method based on proximal detection data offers promising results in agroecological cropping systems, where high responsiveness is a major challenge for site-specific weed management

Cell morphology observations for discriminating between <i>Brettanomyces bruxellensis</i> strains among genetic groups

It is essential to discriminate between B. bruxellensis isolates at the strain level, because stress resistance capacities are strain dependent and also related to the genetic groups (GG). In this work, we investigated further the correlation between genetic groups and cell polymorphism by analysing optical microscopy images via deep learning. A Convolutional Neural Network (CNN) was trained to discriminate between 74 different B. bruxellensis isolates belonging to 4 of the 6 genetic groups described. Compared to the microsatellite analysis, the CNN enabled the prediction of the genetic groups of B. bruxellensis isolates with 96.6 % accuracy in a faster and cheaper way and with the same genetic group affiliations. Based on these very promising results, further research is needed to validate this technique for all genetic groups

Mesocosm evaluation of the competittive ability of common and segetal weed species against barley

International audienceIn conventional cropping systems, arable weed species have always been considered undesirable because their presence can significantly reduce the yield and quality of the crop. Among the weed community, segetal weeds particularly inhabit winter cereal fields and are generally described as rare species (Jauzein, 1997). Following the observed decline in biodiversity in the field, there is a renewed interest in conserving segetal weeds as they represent an invaluable plant heritage as wild relatives of cultivated plants (Fried, 2020). The origin of the regression of these particular annual species is strongly linked to intensive agricultural practices (herbicides, fertilization) but also probably to a poorer competitive ability with recent selected cereal crops unlike common species (Denelle et al., 2020). To study in detail the competitive ability of these two categories of weeds, a wide range of species (7 segetal and 16 common species) was studied in the presence of a spring barley crop (Hordeum vulgare L.). For that purpose, experiments were carried out using mesocosms (rectangular plastic pot) as experimental units. Each mesocosm was divided into three studied zones: one quarter was dedicated to the growth of barley alone (Zone1), another quarter was dedicated to a weed alone (Zone4) and the remaining half was a mixture of barley and weeds (Zone2&3). For each mesocosm, only one weed species was considered. In total, on the 23 mesocosms, different competitiveness traits were measured during the plant development cycle between March and June 2021: plant height, above-ground biomass and total plant biomass. The calculation of the Relative Competitive Performance index (RCP) with height as a competitive trait revealed different competitive effects caused by weeds and a ranking of the weeds according to their competitive power is proposed and compared to the literature. Under the conditions of our experiment, it does not seem that segetal species showed a lower ability to compete than common specie

Estimation de l’indice foliaire et de la biomasse du blé et des adventices par imagerie visible et machine learning : vers un nouvel indicateur non destructif de la compétition culture-adventices ?

Prod 2019-245h équipe EA équipe SPE GESTAD AGROSUP Prod 2019-245h équipe EA équipe SPE GESTAD AGROSUPNational audienceCette étude propose d’estimer précocement par imagerie deux variables clés dans la gestion des cultures et dans la compétition culture-adventices : l’indice foliaire (LAI) et la biomasse aérienne sèche (BM). Une expérimentation a été conduite au champ pendant la phase végétative d’une culture de blé. Pour chaque peuplement (culture de blé, adventices), les taux de couverture du sol par la végétation (TCc, TCw) ont été déduits du traitement d’image basé sur une technique de machine learning. LAI et BM ont été mesurés de façon destructive. Puis, une calibration a été réalisée entre TC et LAI d’une part et entre TC et BM d’autre part. Ce travail pourrait, à terme, faciliter le diagnostic de l’agriculteur dans sa gestion durable de la flore adventice avec la création d’un nouvel indicateur non destructif de la compétition culture-adventices construit à partir de simples images visibles. This study proposes to estimate very early the leaf area index (LAI) and aerial dry biomass (BM) of crop and weeds by an image approach (RGB images). A field experiment was conducted during the vegetative phase of wheat. For each stand (crop, weeds), the vegetation cover ratios (TCc, TCw) were deduced from visible images and machine learning techniques. LAI and BM were measured with destructive methods. Then, a calibration was performed between TC and LAI and between TC and BM. This work could facilitate the diagnosis of the farmer for a sustainable weed management because it aims to create a new non-destructive indicator of crop-weed competition using simple visible images