Potential for coupling a high spectral resolution/low spatial resolution sensor and a low spectral resolution/high spatial resolution sensor for plant breeding

L’objectif de la thèse est d’explorer le potentiel d’un couplage entre un capteur de haute résolution spectrale/faible résolution spatiale et un capteur à faible résolution spectrale et forte résolution spatiale pour la sélection variétale. Ce système est étudié dans le cadre du phénotypage du maïs en conditions de stress hydrique. L’étude est organisée de la manière suivante : Dans un premier temps, il s’agissait de vérifier l’hypothèse selon laquelle l’utilisation d’une forte résolution spectrale apporte un plus pour le phénotypage dans le cadre de la sélection variétale. Pour cela, deux campagnes expérimentales ont été réalisées en 2017 et 2018. Des spectres ont été acquis au champ en utilisant la spectroscopie visible et proche-infrarouge selon un plan d’expérience comptant au total 10 génotypes connus pour leur tolérance face au stress hydrique. Cette partie montre qu’il est possible de caractériser les comportements des génotypes en situation de stress hydrique tout en décrivant précisément les régions spectrales responsables de cette classification.L’utilisation d’un spectromètre en extérieur induit un manque de répétabilité des mesures. Les conclusions des analyses réalisées sur des spectres portant cette erreur peuvent alors être faussées. Une méthode a donc été développée pour réduire l’erreur de répétabilité à travers l’utilisation d’une série de répétitions de mesures additionnelles au plan d’expérience. Cette méthode modifie l’algorithme d’analyse de variance ASCA en introduisant des projections orthogonales dans l’espace des spectres, en complément des projections orthogonales dans l’espace des individus, réalisées naturellement par l’analyse de variance. L’objectif de la dernière partie était de réaliser le couplage d’un capteur à haute résolution spectrale et faible résolution spatiale (spectromètre Vis-NIR) avec un capteur à faible résolution spectrale et haute résolution spatiale (une caméra RGB) à l’aide d’algorithmes de pan-sharpening pour reconstituer une image hyperspectrale de test. Cette partie comportait deux étapes : une approche par simulation pour comparer les algorithmes de pan-sharpening à l’aide d’une image hyperspectrale et une partie pour proposer une méthode pour reconstruire une image hyperspectrale à partir de la solution de couplage proposée.The objective of the thesis is to explore the potential for coupling a high spectral resolution/low spatial resolution sensor and a low spectral resolution/high spatial resolution sensor for plant breeding. This system is studied in the context of maize breeding under water stress conditions. The study is organized as follows: The first step was to test the hypothesis that the use of high spectral resolution is an advantage for phenotyping in varietal selection. For this purpose, two experimental campaigns were carried out in 2017 and 2018. Spectra were acquired in the field using visible and near-infrared spectroscopy according to an experimental design with a total of 10 genotypes known for their tolerance to water stress. This part shows how to describe genotypes behaviour under water stress conditions by accurately describing the spectral regions responsible for this classification.The use of an outdoor spectrometer leads to a lack of repeatability of measurements. To perform analysis of variance of multivariate data with repeatability error can lead to wrong conclusions. A method has therefore been developed to reduce the repeatability error through the analysis of repeated measures of additional measurements to the experimental design. This method modifies the ASCA analysis of variance algorithm by introducing orthogonal projections into the row-space, in addition to the orthogonal projections into the column-space, which are naturally performed by analysis of variance. The objective of the last part was to couple a high spectral resolution and low spatial resolution sensor (Vis-NIR spectrometer) with a low spectral resolution and high spatial resolution sensor (RGB camera) using pan-sharpening algorithms to reconstruct a hyperspectral test image. This part consisted of two steps: to compare pan-sharpening algorithms using a hyperspectral image by using simulation approach and to propose a method to reconstruct a hyperspectral image from the proposed coupling solution

Potentiel d'un couplage entre un capteur de haute résolution spectrale/faible résolution spatiale et un capteur à faible résolution spectrale/forte résolution spatiale pour la sélection variétale

The objective of the thesis is to explore the potential for coupling a high spectral resolution/low spatial resolution sensor and a low spectral resolution/high spatial resolution sensor for plant breeding. This system is studied in the context of maize breeding under water stress conditions. The study is organized as follows: The first step was to test the hypothesis that the use of high spectral resolution is an advantage for phenotyping in varietal selection. For this purpose, two experimental campaigns were carried out in 2017 and 2018. Spectra were acquired in the field using visible and near-infrared spectroscopy according to an experimental design with a total of 10 genotypes known for their tolerance to water stress. This part shows how to describe genotypes behaviour under water stress conditions by accurately describing the spectral regions responsible for this classification. The use of an outdoor spectrometer leads to a lack of repeatability of measurements. To perform analysis of variance of multivariate data with repeatability error can lead to wrong conclusions. A method has therefore been developed to reduce the repeatability error through the analysis of repeated measures of additional measurements to the experimental design. This method modifies the ASCA analysis of variance algorithm by introducing orthogonal projections into the row-space, in addition to the orthogonal projections into the column-space, which are naturally performed by analysis of variance. The objective of the last part was to couple a high spectral resolution and low spatial resolution sensor (Vis-NIR spectrometer) with a low spectral resolution and high spatial resolution sensor (RGB camera) using pan-sharpening algorithms to reconstruct a hyperspectral test image. This part consisted of two steps: to compare pan-sharpening algorithms using a hyperspectral image by using simulation approach and to propose a method to reconstruct a hyperspectral image from the proposed coupling solution.L'objectif de la thèse est d'explorer le potentiel d'un couplage entre un capteur de haute résolution spectrale/faible résolution spatiale et un capteur à faible résolution spectrale et forte résolution spatiale pour la sélection variétale. Ce système est étudié dans le cadre du phénotypage du maïs en conditions de stress hydrique. L'étude est organisée de la manière suivante : Dans un premier temps, il s'agissait de vérifier l'hypothèse selon laquelle l'utilisation d'une forte résolution spectrale apporte un plus pour le phénotypage dans le cadre de la sélection variétale. Pour cela, deux campagnes expérimentales ont été réalisées en 2017 et 2018. Des spectres ont été acquis au champ en utilisant la spectroscopie visible et proche-infrarouge selon un plan d'expérience comptant au total 10 génotypes connus pour leur tolérance face au stress hydrique. Cette partie montre qu'il est possible de caractériser les comportements des génotypes en situation de stress hydrique tout en décrivant précisément les régions spectrales responsables de cette classification. L'utilisation d'un spectromètre en extérieur induit un manque de répétabilité des mesures. Les conclusions des analyses réalisées sur des spectres portant cette erreur peuvent alors être faussées. Une méthode a donc été développée pour réduire l'erreur de répétabilité à travers l'utilisation d'une série de répétitions de mesures additionnelles au plan d'expérience. Cette méthode modifie l'algorithme d'analyse de variance ASCA en introduisant des projections orthogonales dans l'espace des spectres, en complément des projections orthogonales dans l'espace des individus, réalisées naturellement par l'analyse de variance. L'objectif de la dernière partie était de réaliser le couplage d'un capteur à haute résolution spectrale et faible résolution spatiale (spectromètre Vis-NIR) avec un capteur à faible résolution spectrale et haute résolution spatiale (une caméra RGB) à l'aide d'algorithmes de pan-sharpening pour reconstituer une image hyperspectrale de test. Cette partie comportait deux étapes : une approche par simulation pour comparer les algorithmes de pan-sharpening à l'aide d'une image hyperspectrale et une partie pour proposer une méthode pour reconstruire une image hyperspectrale à partir de la solution de couplage proposée

Analyse d’une quantité massive de données spectrales avec parSketch-PLSDA pour la description de génotypes de tournesols

International audienc

Discriminating between Absorption and Scattering Effects in Complex Turbid Media by Coupling Polarized Light Spectroscopy with the Mueller Matrix Concept

The separation of the combined effects of absorption and scattering in complex media is a major issue for better characterization and prediction of media properties. In this study, an approach coupling polarized light spectroscopy and the Mueller matrix concept were evaluated to address this issue. A set of 50 turbid liquid optical phantoms with different levels of scattering and absorption properties were made and measured at various orientations of polarizers and analyzers to obtain the 16 elements of the complete Mueller matrix in the VIS–NIR region. Partial least square (PLS) was performed to build calibration models from diffuse reflectance spectra in order to evaluate the potential of polarization spectroscopy through the elements of the Mueller matrix to predict physical and chemical parameters and hence, to discriminate scattering and absorption effects, respectively. In particular, it was demonstrated that absorption and scattering effects can be distinguished in the Rayleigh regime with linear and circular polarization from the M22 and M44 elements of the Mueller matrix, correspondingly

Removing spatial effects of spectral dataset acquired into an experimental design by using multivariate analysis of variance

International audienc

Including measurement effects and temporal variations in VIS-NIRS models to improve early detection of plant disease: Application to Alternaria solani in potatoes

Early detection of plant diseases with automated, non destructive and high-throughput techniques is a major objective in plant breeding and crop protection. Near infrared spectroscopy and hyperspectral imaging are proven to be particularly relevant technologies. However, robust discriminant models remains a challenge because of the many uncontrolled sources of variability during the experiment. Indeed, at early stages of most diseases, the temporal variations due to environment and measurement effects can induce signal shifts of greater magnitude than the infection itself, masking the information of interest. Excluding the variations of the measurement environment and the temporal fluctuation of the plant-pathogen interaction can depreciate the model robustness. Here, the problem is addressed in a study of the seven potato cultivars monitored for the presence of early blight disease at 0, 18, 36, and 96 h after inoculation. Three practical corrections are proposed regarding the effect of temporal fluctuations. (i) subclass effect, (ii) kinetic effect of the disease, and (iii) measurement effect. Eventually, the application of EPO-PLSDA to orthogonalise the model regarding temporal variation to produce invariant models proved to be the only suitable and well-performing of the tested solutions. With this approach the disease can be detected from 36 h after inoculation for 6 of the 7 tested cultivars. Classification errors differ among the cultivars but on average are below 25% of errors

Monitoring grape berry maturity by hyperspectral imaging: Potential of robust methods for sugar content prediction

International audienc

Evaluation of a combination of NIR micro-spectrometers to predict chemical properties of sugarcane forage using a multi-block approach

International audienceForage quality is essential in livestock farming and has an important role in the functioning of agricultural farms.& nbsp;Access to biochemical variables provides an estimation of the feed value of crop for animal feed at harvest. Near infrared (NIR) spectroscopy provides measurements indirectly related to biochemical variables. In recent years, several micro-spectrometers have been developed that offer the opportunity to predict such biochemical variables at low cost. In this study, the potential of a combination of micro-spectrometers is evaluated to predict crude protein (CP) and total sugar content (TS) of sugarcane. First, each micro-spectrometer with optimal pre treatments was individually compared to a reference laboratory spectrometer. Then, a combination of micro-spectrometers is proposed and prediction models were established by a multi-block method from data fusion called Sequential and Orthogonalised Partial Least Squares (SO-PLS). For CP, the combination of micro-spectrometers provides model (sep = 0.69%; bias = 0.15%; R-test(2) = 0.910) close to those obtained with the reference spectrometer (sep = 0.56%; bias =-0.13%; R-test(2)& nbsp;= 0.935). For TS, the results obtained with this combination of micro spectrometers (sep = 2.38%; bias =-0.52%; R-test(2) = 0.983) are better than those obtained with the reference spectrometer (sep = 2.59%; bias = 0.41%; R-test(2 & nbsp;)= 0.978). For both chemical variables, the combination of the micro-spectrometers significantly increases the performance of the predictive models compared to the models obtained with the micro-spectrometers independently. Using several low-cost micro-spectrometers, combined with a multi-block method would give results as good as a single laboratory spectrometer with a lower cost.& nbsp;(C) 2022 IAgrE

Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: Case study of grape berry maturity monitoring

International audienceVisible and near infrared spectroscopy (VIS-NIR) is increasingly being transferred from laboratory to industry for in-line and portable applications in various domains. By intensively using VIS-NIR spectroscopy, some abnormal observations may certainly arise. It is then important to properly handle outliers to elaborate effective prediction models. The objective of this study is to investigate the potential of using a robust method called Roboost-PLSR to improve prediction model performances for a viticulture application. This work focuses on a case study to predict sugar content in grape berries of three different grape varieties of Vitis Vinifera in a maturity monitoring context. Hyperspectral images were acquired of grape berries of Syrah, Fer-Servadou and Mauzac varieties. Reference measurements of sugar levels were made in the laboratory by densimetric baths