Description

Context and research challenge

The fruit sector's competitive position in Flanders can only be retained by an increase in rentability of the fruit growing companies. While human judgment might always be needed to some degree, autonomous robots, including drones, could take a lot of the hard, repetitive work out of fruit farming. By using the power of the digital age to handle large amounts of data, crops can be dealt with as individual plants rather than on a field-wide basis. Applying fertilizer, irrigation, herbicides, and pesticides in more targeted ways could reduce costs for farmers and ensure plants get the right amount of each treatment. Furthermore, efficient and accurate flower and fruit counting supports yield prediction. This latter has importance to the industry in planning market needs and resources, such as crop insurance purposes, packing materials, planning harvest, and employees. Furthermore, the industry needs to book transport, estimate cash-flow budget, and plan delivery estimates to ensure a faster turnaround to lessen the waste of time and resources.

The required data can be generated by sensors mounted on tractors/robots or drones or through satellite images. Camera and sensor technology have recently been evolving steadily and offer many opportunities to increase the fruit sector's competitiveness. The applicability or success of precision agriculture depends on the availability of appropriate data and how information is translated into treatment measures.

Approach

Hyperspectral and RGB drone and tractor-based data is available from ongoing projects in which the aim is to (1) detect Red Palm Weevil (RPW) infestations in palm trees in Spain and Saudi Arabia; (2) detect fire blight infections in pear orchards in Belgium; (3) monitor pear fruit production throughout the season in Belgium. 

By extending existing artificial intelligence algorithms for hyperspectral imagery, this research topic aims to develop a method to detect different objects in a fruit/ palm tree.

  • A map of leaf objects can be used to steer pruning and precision spraying in fruit trees and to analyze the spectral behavior of different leaf age classes in palm trees
  • A map of fruit objects will help in predicting yield
  • A map on flower objects can steer flower/ fruit thinning
  • A map with healthy and diseased objects can steer precision herbicide and pesticide spraying, precision irrigation and fertilization.

An expected outcome is the development of an AI based image processing chain for (diseased) object detection in fruit orchards.

With this call, we invite researchers to submit their resumé (including track-record) and a one-page project description, that will be the basis for selecting candidates with whom we will collaborate for developing a competitive MSCA-PF proposal. 

 Collaborations

King Abdullah University of Science and Technology (KAUST), Saudi Arabia - Phoenix research Station (PRS), Spain - Pcfruit, St Truiden, Belgium - WUR, the Netherlands - KULeuven, Belgium - IMEC, Belgium - FlandersMake, Belgium

Deadline application to VITO

Interested candidates should submit their resume (incl. track record) and a one-page note describing the project for which a Marie Curie grant will be applied, as soon as possible and no later than Friday 2 April 2021 17h Brussels time.

Supervisor

Successful candidates will be supervised by dr. Stephanie Delalieux (Scopus h-index: 18): Stephanie Delalieux obtained her PhD degree in Bioscience Engineering, Department of Biosystems in 2009 from the KULeuven (BE). During her PhD, she zoomed in on early stress detection in fruit orchards using hyperspectral image analysis. Today, Stephanie continues her passion for the field as a senior researcher at VITO Remote Sensing. Her main research interests include the use and application of high spectral, high spatial and high temporal resolution remote sensing data for precision agriculture and biodiversity applications. She has coordinated several research and commercial projects and has supervised more than 10 MSc and 4PhD students.

Further information can be obtained from dr. Stephanie Delalieux via e-mailStephanie.Delalieux@vito.be

Deadline MSCA-PF 2021

Wednesday 15 September 2021 17h Brussels time.

Target start date

The EU informs the results on the MSCA-PF applications in February 2022. Successful candidates are expected to be available to start within the following two months and no later than summer 2022.

Qualification

We invite applicants to propose a more detailed and focused research approach within the scope of this MSCA-PF Fellowship as a part of their application. We are primarily looking for experienced researchers who wish to use this period as an opportunity to further develop their research and skills, and to develop longer-term research collaborations with VITO and other institutions conducting research in the field.

The candidates as in principle must be eligible for a Marie Curie Postdoctoral Fellowship – please refer to the conditions to be set-out in the Horizon Europe MSCA-PF-2021 Work Programme, including taking into account the new MSCA Green Charter principles.

The following assets will be advantageous:

  • An excellent track record in research, necessary for being able to develop a competitive Marie Curie Fellowship application;
  • Already published relevant research work in prestigious scientific journals;
  • An open and cooperation-oriented nature, but with strong abilities for independent research work;
  • highly proficient in spoken and written English.
Offer

Initially, we offer assistance in developing competitive Marie Curie Individual Fellowship proposals.

Then, to successful applicants to the Marie Curie programme, we offer;

  • An exciting opportunity at VITO, the independent Flemish research organisation driven by the major global challenges. Our goal? To accelerate the transition to a sustainable world;
  • Participation in a dynamic professional research & innovation community;
  • Flexible working conditions;
  • An inclusive and friendly work environment;
  • On-boarding assistance and other services.

 

Requisition

Location: 
Mol
Jobfield: 
Postdoc
ID: 
32500