The post-doc offers

The post-doc offers

#DigitAg offers 100%-funded interdisciplinary post-doc topics

IMPORTANT: applicants must not be :
- a former labellised, co-funded or other PhD student from a #DigitAg member unit
- a former PhD student supervised/supervised by a #DigitAg associate researcher


Offres de post-docs

The topics displayed have been selected and funded by #DigitAg
Want to find out more? Get in touch with the contact indicated for the topic you are interested in

Selected topics:


Maths and its applications - Life and Environmental sciences

Use of temporal series of hyperspectral images for early assessment of septoria symptoms in wheat

Keywords: Durum wheat, septoria, NIRS, Hyperspectral imaging

  • Contact: Martin ECARNOT - martin.ecarnot [AT]
  • Supervisors: Jean-Michel Roger, Itap , INRAe - Martin Ecarnot, Agap, INRAe
  • Units: AGAP - PHIM
  • #DigitAg: Axe 5 : Fouille de données, analyse de données, extraction de connaissances, Axe 2 : Innovations en agriculture numérique, Challenge 2 : Le phénotypage rapide, Challenge 3 : La protection des cultures

There is a growing interest in phenotyping plant resistance to leaf diseases using optical methods in order, on the one hand, to specify the status of the plant with respect to the pathogen (in a range from susceptible to resistant) and, on the other hand, to have robust methods characterising these interactions in a wide range of environmental situations.
In order to investigate the capacities of hyperspectral imaging (HSI) to characterise the symptoms of a fungal disease (septoria in wheat), a thesis was carried out (2020-2023) within a group of researchers bringing together specialists in HSI, infrared spectra processing and phytopathology (UMRs ITAP, PHIM and AGAP Institut). This work focused on the processing of HSI time series and led to the development of a method (based on Moving Window Principal Component Analysis, MWPCA) that clearly distinguishes the average kinetics of healthy and diseased leaves using visible and near-infrared spectra. The perspectives produced during this thesis open up two lines of research: (i) characterising the pĥenomenon at the individual scale (and not just a group average), (ii) achieving earlier detection of the disease and (iii) taking into account spatial dimension of the image.
Therefore, the first objective of this postdoctoral research project is to explore these two lines of research by applying alternative data processing algorithms to the dataset generated during the thesis, in order to improve the sensitivity and earliness of the measurement of disease establishment. To this end, a second and more operational objective is to improve the overall methodology developed in the thesis by proposing a more robust  and parsimonious methodology, to assess the potential of a tool that could be deployed in the field.

Human and social sciences - Life and environmental sciences

Determinants, modalities and value of data sharing by farmers and SMEs in blockchains for transparent and sustainable food supply chains
filed post-doc

Keywords: Blockchain, traceability, transparency, food supply chain, power games, data sharing, digital, valuation, participatory sciences

  • Contact: Florent Saucède - florent.saucede[AT]
  • Supervisors: Florent Saucède, Moisa, Institut Agro - Montpellier SupAgro - Léa Tardieu, Tetis, Inrae
  • Units: Moisa, Tetis
  • Cofunding: Projet ANR JCJC
  • #DigitAg: Axe 1 : Impact des technologies de l'information et de la communication sur le monde rural, Axe 2 : Innovations en agriculture numérique,Axe 4 : Système d’information, stockage et transfert de données, Challenge 7 : Intégration de l’agriculture dans les chaînes de valeur, Challenge 5 : Les services de conseil agricole

Consumers’ mistrust of complex food systems increases with the number of food scandals and sanitary crises. To address this, producers and retailers are experimenting with blockchain, “a digital, decentralised and distributed ledger in which transactions are recorded and added in chronological order with the goal of creating permanent and tamperproof records” (Treiblmaier, 2018, p. 547). It offers a novel way to trake and trace products along food supply chains (FSCs), thank’s to the members’ contribution and joint construction of immutable information that can be visible to all and communicated to consumers. It has the potential to improve the functioning, digitisation, automation and sustainability of FSCs. While blockchain allows for the design of new collective and participative modes of organisation, it is also a monitoring system whose transparency is co-constructed through the sharing of sensitive data previously kept private. The conditions to enable these potentials and minimise the risks of this technology are therefore unknown. 
The post-doctorate is part of an ANR JCJC project proposal that assesses the potential of blockchain to make FSCs more participative, transparent and efficient, to contribute to the transition towards sustainable food systems. Focusing on farmers and SMEs, the post-doctorate aims to better understand the conditions, modalities, risks, reluctances, and individual and collective costs and benefits of data sharing in FSCs for traceability, transparency and valorisation of practices. The determinants and valorisation of data sharing are examined in the context of power dynamics within FSCs. Mobilising management, economic, environmental and data sciences in a participatory approach with farmers and SMEs, the project aims to develop a grid structuring the co-construction of information for sustainable, transparent and efficient FSCs, and to identify the data necessary for its construction, while helping to prepare these producers for the challenges of the spread of such systems of FSCs’ transparency.

Sciences for the Engineer - Life and Environmental sciences

A hybrid approach to combining biophysical modeling and remote-sensing derivatives model 3D canopy architecture in vineyards for differential management

Keywords: canopy architecture, LAI, vineyards, machine-learning, LiDAR, Sentinel-2, Radiation transfer models

  • Contact: James TAYLOR - james.taylor [AT]
  • Supervisors: James Taylor, Itap, Inrae - Jean-Baptiste Feret, Tetis, Inrae
  • Units: Itap - Tetis
  • #DigitAg: Axe 6 : Modélisation et simulation (systèmes de production agricole), Axe 3 : Capteurs, acquisition et gestion de données, Challenge 6 : La gestion des territoires agricoles, Challenge 3 : La protection des cultures

Measuring the size, shape and density of vineyard canopies is not easy. The canopy will continually evolve over the season and the 3D characteristics of the canopy will strongly influence the need for and the efficacy of crop protection actions during the season. Proximal sensors can provide high-resolution information on the 3D canopy structure, but are limited in the spatial and temporal resolution of deployment. Satellite imagery provides high-resolution spatio-temporal information on vineyard vigour, but the image information is only partly influenced by canopy shape. Therefore, neither sensing system, in its native form, is capable of providing relevant vineyard information to support in-season, differential crop protection strategies. This project will use derivatives from remotely sensed imagery, obtained via time-series analysis of vegetation indices and from inverse radiative transfer models, to generate models that can predict the 3D characteristics of vineyards over a large area. The calibration and validation data for these models will be derived from high-resolution LiDAR data at selected points. The processing of these LiDAR data will be based on new algortihms that provide 3D information on the vineyard canopies. The modelling of the 3D canopy characteristics with the remote-sensing derivatives will be performed using a mixture of linear and non-linear machine-learning methods. Once a stable model has been found, the predicted, large-scale 3D canopy information will be substituted into exisiting spray deposition models to evaluate if the 3D canopy predictions are of sufficient quality to be used for management.
The project will be built of existing data sets (Sentinel 2 imagery and LiDAR campaigns by UMR ITAP) and existing programmed algorithms for data processing (time-series, LiDAR and radiative transfer models from both UMR ITAP and TETIS). It seeks to connect several previous #DigitAG projects to generate useful, operational vineyard information.

Co-design, adaptation and dissemination of high-low-techs to understand and support water management in the agro-ecological transition

Keywords: Agricultural water management ; Agroecology ; High-low-tech sensors ; Participatory design

  • Contact: Léo Garcia - leo.garcia [AT]
  • Supervisors: Léo Garcia, UMR ABSys, Institut Agro Montpellier – Crystèle Leauthaud, G-eau, Cirad
  • Units: ABSys - G-eau - Itap
  • #DigitAg: Axe 2 : Innovations en agriculture numérique, Axe 1 : Impact des technologies de l'information et de la communication sur le monde rural, Axe 3 : Capteurs, acquisition et gestion de données, Challenge 1 : Le challenge agroécologique, Challenge 5 : Les services de conseil agricole

Agroecology is emerging worldwide as an alternative to meet the challenges of agricultural sustainability, notably with regard to water resources, subject to strong anthropic and climatic pressures. The transition from conventional production systems to agroecological systems faces various obstacles, particularly because of the increased complexity of agroecosystems that it may involve. Recent advances in mass information technologies (on-board electronics, IoT, wireless sensor networks) offer opportunities for the development of new measurement systems, more technically and economically accessible to the agricultural sector. However, the use and adoption of such tools remain conditioned by the agricultural contexts in which they are implemented. We believe that the co-design of new low-cost digital technologies can improve the understanding of water flows and support their management within agroecological cropping systems. The objectives of the project are to i) assess the constraints and needs of producers regarding water management, specific to different agroecological contexts, ii) adapt existing technologies (or develop them if necessary) through a participatory method involving farmers to meet the challenges of the systems studied, and iii) facilitate the adoption and dissemination of digital innovations by supporting the stakeholder networks established (monitoring, consulting, training). Two study sites, in Occitanie and California, representing different agricultural contexts (socio-economic environments and means of production), cropping systems (annual, perennial, agro-ecological practices) and contrasted levels of hydric constraint, will enable these methods to gain in genericity and robustness for the production of references to support the agroecological transition

High-throughput phenotyping of fruit tree genetic diversity for better adaptation to climate change

Mots-clé: Digital phenotyping, genetic screening, fruit trees, cross-species generalization

  • Contact: Marie Weiss - marie.weiss [AT]
  • Supervisors: Marie Weiss, UMR Emmah, Inrae - Evelyne Costes, UMR Agap, Inrae
  • Units: Emmah - Agap
  • #DigitAg: Axe 2 : Innovations en agriculture numérique, Axe 3 : Capteurs, acquisition et gestion de données, Challenge 2 : Le phénotypage rapide, Challenge 1 : Le challenge agroécologique, Challenge 3 : La protection des cultures

This project aims to characterize the resilience of fruit trees via digital phenotyping of a certain number of traits related to flowering and tree architecture based on the combination of the skills in image analysis (AGAP-PHENOMEN, EMMAH-CAPTE), genetics (GAFL) and architectural analysis (AFEF) of the four teams involved. In addition to methodological developments in stereovision and RGB data processing to assess these traits, this project is interested in the fusion of information generated by these data and the generalization and transferability of these traits and methods between species. Several approaches (machine and deep learning, mathematical morphology, statistics) will be combined. This project is based on the acquisition of datasets in three core collections (peach, apricot, apple) each of them comprising more than 150 different genotypes, thus ensuring a representativeness of the variability of flowering and tree structure by differences in age and contrasting environments. It also aims to establish temporal consistency between past measurements (visual notations) and digital phenotyping.
In terms of expected outputs, we aim to contribute to the phenotyping of complex and integrative traits (improved accuracy and throughput, access to new traits not accessible manually), and to the genetic screening of resilience by determining a typology of trees that can maintain production in the face of environmental perturbations.

Modification date: 08 February 2024 | Publication date: 12 October 2023 | By: GL