[PhD’s Corner] Maxime Ryckewaert: Potential of aerial acquisition of high spectral /low spatial resolution information for plant phenotyping

 Maxime Ryckewaert has successfully defended his PhD Thesis on 7 November 2019 at Montpellier SupAgro

Hi, I’m Maxime Ryckewaert and I am working on the “Potential of aerial acquisition of high spectral /low spatial resolution information  for plant phenotyping”.
I’m hosted at Irstea, in the ITAP joint research unit, in the team of the optical sensors for complex media (for example: the vegetation). I obtained a Master’s degree in Physics and which gives me a different point of view on scientific questions related to Agriculture. My career path is fairly unusual because I worked as an engineer for two years in another joint research unit ‘UMR AMAP’ that’s why I decided to continue in research. This is a Cifre agreement with Irstea and Limagrain., and a #DigitAg labeled PhD.

Agriculture must be adapted to global climatic change like drought and must deal with water-stress. So, it’s important to create new varieties more resistant and better suited to drought. How to select these kinds of new varieties? In order to identify and describe stress tolerance of different corn varieties, we can obtain the information by the spectral reflectance which is the reflected radiation from vegetation.

These days, the existing solutions aren’t adapted to plant breeding. On the one hand, we have the hyperspectral imaging which provide a lot of information about the spectral reflectance. But the cameras are too heavy and too expensive. On the other hand, we have multispectral imaging with little information about the spectral reflectance but the sensors are lighter and cheaper. The objective of this thesis is to develop a cheaper and lighter system by keeping the spectral information in order to put on a UAV and to cover several hectares of an experimental field.

I decided to make PhD studies because it’s interesting to approach agronomical issues from a different angle.  I like to evolve with the topic by bringing new hypotheses, corroborating them and creating new methods. I’m at the end of my first year of PhD studies. I’m analysing data from the first experimental campaign. These results will check the relevance of our approach or readjust it if necessary.

Potential of aerial acquisition of high spectral /low spatial resolution information  for plant phenotyping.

  • Start Date: 1st november 2016
  • Defence : November 2019
  • University: MUSE Montpellier University of Excellence / Montpellier SupAgro
  • PhD School:  GAIA, APAB, Montpellier (France)
  • Field(s): Optics, Spectrometry, Process Engineering, Agroresources
  • Doctoral Thesis Advisor: Jean-Michel Roger (UMR ITAP)
  • Co-supervisors :  Alexia Gobrecht (UMR ITAP), Nathalie Gorretta (UMR ITAP), Fabienne Henriot (Limagrain)
  • Funding: Cifre Agreement Irstea – Limagrain
  •  #DigitAg: Labeled PhD – Challenge 2 (Digital solutions to optimize the genotype in changing production systems and markets)

Keywords: Spectrometry, Phenotyping, Water stress, Chemometrics, Drone

Abstract:

The operational objective of this thesis is to study the opportunities offered by the VIS/NIR spectrometry couple with an aerial acquisition system to address the needs of new tools for high-throughput phenotyping. The underlying application is to identify and describe new genotypes with better behavioural response under water-stress. From a technological point of view, it also represents a potential way to have access to the high spectral resolution of vegetal cover. However, the spectral information must be relevant and exploitable for phenotyping, and therefore, processing will be optimised by developing:

  • methods for extracting appropriate parameters for breeding genotype;
  • methods that will improve in spectral/spatial resolution of signals.

Maintaining a high degree of spectral resolution is the key factor to produce models.

In this context, it would provide answers to this following scientific question:

How can a low spatial/high spectral resolution sensor be coupled with a mobile vector in order to produce high spatial /high spectral resolution information allowing to describe vegetal response under stress?

Thus, the research objectives are:

  • To test the hypothesis that with a total spectral signature of vegetation, the crop monitoring or the extraction of phenotyping traits are more robustness and more sensitive with a small variation of genotype response under stress;
  • To define acquisition protocols and to develop processing methods of these spectral information to guarantee enough spectral and spatial resolution for phenotyping;
  • To approve methodology for identification and precise characterisation of different corn cultivars under water stress.

Contact:  maxime.ryckewaert [AT] irstea.fr – Tél : (+33) 4 67 16 65 00

NetworksResesearchgate –  Linkedin

Communications /Publications

The impact of the spatial resolution of highly resolved spectral data on pan-sharpening methods to reconstruct a hyperspectral image.
Maxime Ryckewaert, Julien Morel, Jean-Michel Roger, Alexia Gobrecht, Fabienne Henriot and Nathalie Gorretta (2017). EFITA 2017, Montpellier (France), July 2nd-6th (European conference dedicated to the future use of ICT in the agri-food sector, bioresource and biomass sector, Session 5A: Sense&Rob1: sensing, robotics and electronics for agriculture (I))

Abstract: Pan-sharpening methods have been developed to increase the spatial resolution of the multispectral information by fusing a panchromatic image (i.e. high spatial /low spectral resolution) with a multispectral one. In recent years, methods have been proposed for hyperspectral and multispectral data fusion. These methods are generally used for satellite data with limited spatial resolution. In order to overcome the spatial resolution, sensors can be embedded on Unmanned Aerial Vehicles. In this presentation, we propose to study the impact of the spatial resolution of highly resolved spectral data on pan-sharpening methods in order to reconstruct a hyperspectral image and to choose the best combination of available cheap sensors.

ANOVA-Simultaneous component analysis on vegetation spectra data acquired into an experimental design.
Ryckewaert Maxime, Roger Jean-Michel, Henriot Fabienne, Gorretta Nathalie, Gobrecht Alexia. HelioSpir, 27/11/2017 (communication orale.

Multivariate analysis of variance of vegetation spectra dataset included into an experimental design by using ANOVA-SCA and ANOVA-Target Projection.
Ryckewaert Maxime, Gorretta Nathalie, Henriot Fabienne, Gobrecht Alexia, Roger Jean-Michel. Conférence SFPT, 18/05/2018 (communication orale).