Maxime Ryckewaert- Researcher
- Researcher at French Agricultural Research Centre for International Development
Maxime Ryckewaert
- Researcher
- Researcher at French Agricultural Research Centre for International Development
Researcher @Cirad
UMR AGAP
About
46
Publications
5,852
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505
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Introduction
I'm a researcher in data analysis (Machine Learning, Statistics, Multivariate Data Analysis) including supervised/unsupervised methods, signal processing, data modelling, experimental design, data fusion.
Current institution
Additional affiliations
September 2023 - March 2025
National Institute for Research in Digital Science and Technology
Position
- Starting Research Position
November 2019 - present
Publications
Publications (46)
Background: Water resource is a major limiting factor impacted by climate change that threatens crop production and quality. Understanding the ecophysiological mechanisms involved in the response to water deficit is crucial to select new varieties more drought tolerant. A major bottleneck hampering such advances is the lack of methods for measuring...
This article presents a hyperspectral imaging (HSI) database of healthy leaves and leaves infected with Zymoseptoria tritici fungal pathogen responsible for leaf blotch (Lb) disease. Leaves of two durum wheat genotypes were studied under controlled conditions to track the evolution of Lb disease and capture significant spectral and spatial differen...
The rapid expansion of citizen science initiatives has led to a significant growth of biodiversity databases, and particularly presence-only (PO) observations. PO data are invaluable for understanding species distributions and their dynamics, but their use in Species Distribution Models (SDM) is curtailed by sampling biases and the lack of informat...
This article proposes a generic framework to process jointly the spatial and spectral information of hyperspectral images. First, sub-images are extracted. Then each of these sub-images follows two parallel workflows, one dedicated to the extraction of spatial features and the other dedicated to the extraction of spectral features. Finally, the ext...
This study focuses on the promising use of biospeckle technology to detect water stress in plants, a complex physiological mechanism. This involves monitoring the temporal activity of biospeckle pattern to study the occurrence of stress within the leaf. The effects of water stress in plants can involve physical and biochemical changes. Some of thes...
Spectral data from multiple sources can be integrated into multi-block fusion chemometric models, such as sequentially orthogonalized partial-least squares (SO-PLS), to improve the prediction of sample quality features. Pre-processing techniques are often applied to mitigate extraneous variability, unrelated to the response variables. However, the...
A hyperspectral imaging database was collected on two hundred and five grape plant leaves. Leaves were measured with a hyperspectral camera in the visible/near infrared spectral range under controlled conditions. This dataset contains hyperspectral acquisition of grape leaves of seven different varieties. For each variety, acquisitions were perform...
This dataset consists of three groups of hyperspectral images of apple tree plants. The first group of images consists of a temporal monitoring of seven apple tree plants, infected with fire blight (Erwinia amylovora), and six control plants over a period of 15 days. The second group of images includes a temporal monitoring of three infected plants...
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 uncontrolle...
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 di...
In the dataset presented in this article, two hundred and seventy four trays containing one hundred berries were measured by a hyperspectral camera in the visible/near-infrared spectral domain. This dataset was formed to study the use of hyperspectral imaging for maturity monitoring of grape berries [2]. This dataset contains reflectance spectra fr...
In different disciplines, visible and near infrared spectroscopy (VIS-NIR) is increasingly used for in-line and handheld applications. There is a risk that abnormal observations may occur. It is then important to handle correctly the outliers to develop effective prediction models. The objective of this study is to examine the potential of a robust...
In recent years, climate fluctuations have been increasingly extreme, affecting agricultural production. The development of digital agriculture driven by new intelligent sensors is one of the privileged paths to improve farm management. Assessing transpiration E and stomatal conductance gs in real time with optical instruments is a real challenge t...
Forage quality is essential in livestock farming and has an important role in the functioning of agricultural farms.
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...
Recently, a novel robust PLSR method was developed to address the problem of outliers in the data. In this paper, an extension of this method, called RoBoost-PLS2-R is proposed to predict multi-response variables. Robustness and efficiency of this new approach have been validated on two simulated data sets and one real data set containing different...
Visible 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....
Hyperspectral imaging is an emergent technique in viticulture that can potentially detect bacterial diseases in a non-destructive manner. However, the main problem is to handle the substantial amount of information obtained from this type of data, for which reliable data analysis tools are necessary. In this work, a combination of multivariate curv...
Spectroscopy is today and for two decades strongly used in many fields (pharmacy, agriculture, process, medicine…). This use in a very large number of applications is linked to the great spectral richness of the measurement and therefore to the large amount of accessible chemical information. For plant breeding, spectral reflectance in the visible...
In precision agriculture and plant breeding, the amount of data tends to increase. This massive data is becoming more and more complex, leading to difficulties in managing and analysing it. Optical instruments such as NIR Spectroscopy or hyperspectral imaging are gradually expanding directly in the field, increasing the amount of spectral database....
Close-range spectral imaging (SI) of agricultural plants is widely performed for digital plant phenotyping. A key
task in digital plant phenotyping is the non-destructive and rapid identification of drought stress in plants so as to
allow plant breeders to select potential genotypes for breeding drought-resistant plant varieties. Visible and nearin...
Analyse de une quantité massive de données spectrales avec la méthode parSketch-PLSDA
New instruments to characterize vegetation must meet cost constraints while providing accurate information. In this paper, we study the potential of a laser speckle system as a low-cost solution for non-destructive phenotyping. The objective is to assess an original approach combining laser speckle with chemometrics to describe scattering and absor...
A method to reduce repeatability error in multivariate data for Analysis of variance-Simultaneous Component Analysis (REP-ASCA) has been developed. This method proposes to adapt the acquisition protocol by adding a set containing repeated measures for describing repeatability error. Then, an orthogonal projection is performed in the row-space to re...
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
orga...
This study aims to investigate the combination of speckle pattern analysis, polarization parameters, and chemometric tools to predict the optical absorption and scattering properties of materials. For this purpose, an optical setup based on light polarization and speckle measurements was developed, and turbid samples were measured at 405 and 660 nm...
The leaf coverage surface is a key measurement of the spraying process to maximize spray efficiency. To determine leaf coverage surface, the development of optical micro-sensors that, coupled with a multivariate spectral analysis, will be able to measure the volume of the droplets deposited on their surface is proposed. Rib optical waveguides based...
The main objective of an experimental design is to verify assumptions or answer scientific questions (Oehlert, 2000). An experiment can access to the difference between treatments by studying one or more factors on the variance of the observed data (Kirk, 1982).
Over the past decade, chemometrics methods have been developed to take advantage of an...
Background
Plant science uses increasing amounts of phenotypic data to unravel the complex interactions between biological systems and their variable environments. Originally, phenotyping approaches were limited by manual, often destructive operations, causing large errors. Plant imaging emerged as a viable alternative allowing non-invasive and aut...
Agrivoltaic systems, consisting of the combination of photovoltaic panels (PVPs) with crops on the same land, recently emerged as an opportunity to resolve the competition for land use between food and energy production. Such systems have proved efficient when using stationary PVPs at half their usual density. Dynamic agrivoltaic systems improved t...
