[PhD’s Corner] Hugo Deléglise: Improving food security systems by linking heterogeneous data. The case of agricultural production in West Africa

Hugo is one of the #DigitAg co-funded PhDs

My name is Hugo Deléglise, I obtained a degree in fundamental mathematics at the Faculty of Sciences of Montpellier and a master MIASHS (mathematics and computer science applied to the humanities and social sciences) at the Faculty of Arts of Montpellier.

During my master, I did 2 years of internship at Ird on the subject: “Optimization of the collection, the management and the processing of data collected within the framework of a project of operational research on the onchocerciasis “. These last years of studies and this internship in the research confirmed me the desire to use my methodological and technical knowledge to answer concrete problems of development.

Improving food security systems by linking heterogeneous data – The case of agricultural production in West Africa

  • Start Date:  October 2018
  • University:  University of Montpellier
  • PhD School: I2S –  Information, Structures, Systèmes
  • Field(s): Computer Sciences
  • Doctoral Thesis Advisor’s): Agnès Bégué, Mathieu Roche (Cirad, Tetis) et Maguelonne Teisseire (Irstea Tetis)
  • Funding: #DigitAg – Cirad
  • #DigitAg: Axis 5  – Challenge 8

Keywords: machine learning, heterogeneous data, food security, agricultural production, West Africa


This thesis aims at the improvement of Food Security Monitoring systems through the use of heterogeneous data, focusing on the management of agricultural production risks. While agroclimatic data (e.g., satellite imagery, climate information, etc.) has been widely used for this task, the use of data coming from different domains (i.e., household surveys, social media, press, business analyses) has often been neglected. Remote sensing data is widely used for real time monitoring of vegetative growth, but is not sufficient to explain complex food safety-risk phenomena. The aim of this thesis is twofold: (i) to define innovative data mining techniques that will be able to exploit this heterogeneous data context. To reach this goal, three phases have been identified: (a) automatic discovery of spatial features from heterogeneous data, (b) features linking (i.e., through the definition of new similarity measures between features) and (c) data mining (i.e., through the definition of new network analysis, clustering and deep learning techniques) ; (ii) to show how remote sensing data can be enriched by linking it to data from different domains in order to make it more suitable for food safety-risk analysis tasks. During this thesis, we will focus on studies carried out in Burkina Faso, by exploiting satellite (with vegetation and climate features), economic, and textual data. The analytical framework will be based on retrospective analysis, focusing on the crop failures of 2007 and 2011 in Burkina Faso as major cases of studies. We will possibly extend our study to other areas, using data collected in Senegal. Given the interdisciplinary path at the basis of this work, the results of the analysis and the defined techniques are expected to generate significant interest in socio-economic, remote sensing, and data mining fields. During the PhD period, the student will also participate in short term missions (e.g., periods of two or three weeks) to West Africa, working with experts in the field of remote sensing and food security.


Contact:  hugo.deleglise [AT] cirad.fr​