Search Results - "Ryckewaert, Maxime"

Applying the maximum entropy principle to neural networks enhances multi‐species distribution models.

Authors: Ryckewaert, Maxime Marcos, Diego Botella, Christophe et al.

Source: Methods in Ecology & Evolution. May2026, Vol. 17 Issue 5, p1655-1670. 16p.

Using Dynamic Laser Speckle Imaging for Plant Breeding: A Case Study of Water Stress in Sunflowers.

Authors: Bouzaouia, Sherif Ryckewaert, Maxime Héran, Daphné et al.

Source: Sensors (14248220). Aug2024, Vol. 24 Issue 16, p5260. 17p.

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

Authors: Ducanchez, Arnaud Ryckewaert, Maxime Heran, Daphne et al.

Source: Sensors (14248220). Dec2022, Vol. 22 Issue 23, p9355. 12p.

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A New Optical Sensor Based on Laser Speckle and Chemometrics for Precision Agriculture: Application to Sunflower Plant-Breeding.

Authors: Ryckewaert, Maxime Héran, Daphné Faur, Emma et al.

Source: Sensors (14248220). Aug2020, Vol. 20 Issue 16, p4652-4652. 1p.

Coupling Waveguide-Based Micro-Sensors and Spectral Multivariate Analysis to Improve Spray Deposit Characterization in Agriculture.

Authors: Taleb Bendiab, Anis Ryckewaert, Maxime Heran, Daphné et al.

Source: Sensors (14248220). Oct2019, Vol. 19 Issue 19, p4168. 1p.

A novel approach to combine spatial and spectral information from hyperspectral images.

Authors: Gaci, Belal Abdelghafour, Florent Ryckewaert, Maxime et al.

Source: Chemometrics & Intelligent Laboratory Systems; Sep2023, Vol. 240, pN.PAG-N.PAG, 1p

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

Authors: Abdelghafour, Florent Sivarajan, Sajeevan Radha Abdelmeguid, Ingi et al.

Source: Computers & Electronics in Agriculture; Aug2023, Vol. 211, pN.PAG-N.PAG, 1p

Physiological variable predictions using VIS–NIR spectroscopy for water stress detection on grapevine: Interest in combining climate data using multiblock method.

Authors: Ryckewaert, Maxime Héran, Daphné Simonneau, Thierry et al.

Source: Computers & Electronics in Agriculture; Jun2022, Vol. 197, pN.PAG-N.PAG, 1p

RoBoost-PLS2-R: An extension of RoBoost-PLSR method for multi-response.

Authors: Metz, Maxime Ryckewaert, Maxime Mas-Garcia, Silvia et al.

Source: Chemometrics & Intelligent Laboratory Systems; Mar2022, Vol. 222, pN.PAG-N.PAG, 1p

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

Authors: Courand, Aldrig Metz, Maxime Héran, Daphné et al.

Source: Chemometrics & Intelligent Laboratory Systems; Feb2022, Vol. 221, pN.PAG-N.PAG, 1p

Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress.

Authors: Ryckewaert, Maxime Gorretta, Nathalie Henriot, Fabienne et al.

Source: Computers & Electronics in Agriculture; Oct2021, Vol. 189, pN.PAG-N.PAG, 1p

A generic workflow combining deep learning and chemometrics for processing close-range spectral images to detect drought stress in Arabidopsis thaliana to support digital phenotyping.

Authors: Mishra, Puneet Sadeh, Roy Ryckewaert, Maxime et al.

Source: Chemometrics & Intelligent Laboratory Systems; Sep2021, Vol. 216, pN.PAG-N.PAG, 1p

PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments.

Authors: Valle, Benoît Simonneau, Thierry Boulord, Romain et al.

Source: Plant Methods. 11/8/2017, Vol. 13, p1-17. 17p.