Development of artificial vision based intelligent tools for supervising the wellbeing of animals
2D/3D image processing, segmentation and classification, deep learning
Training level: Master 2 and/or Engineer degree in computer science, IT or related field
Good knowledge on machine learning techniques and image processing
Strong capability of coding using Python or C/C++ or Matlab
Good communication skills
ISEN-Lille, 41 boulevard Vauban 59800 Lille, France/ IEMN CNRS laboratory 59650 Villeneuve-d'Ascq/ Gènes Diffusion – Institut Pasteur de Lille.
Duration 3 years starting from 1st October 2019.
Applicationsend your CV and your cover letter before June 10, 2019 to:
Dr. Halim Benhabiles email@example.com (Supervisor)
Dr. Feryal Windal firstname.lastname@example.org (Supervisor)
Dr. Dominique Collard email@example.com (Director)
Pr. AbdelmalikTALEB-AHMED Abdelmalik.Taleb-Ahmed@uphf.fr (Co-director)
Context and goals
The new technologies based on artificial intelligence, IOT, big data, robotics and advanced analysis allow the development of precision agriculture. Offering to farmers tools to observe, measure and analyze the needs of their livestock farming as well as of their staff will certainly improve the management of the resources. Moreover, such tools will lead to reduce the environmental impact and avoid wastage.
The main goal of this thesis is to develop new tools offering to farmers a more precise advice in order to enhance livestock farming efficiency both economically and ecologically. More specifically, beyond the current collected data in the farms, it is possible to exploit images and/or videos to capture several useful information and supervise the wellbeing of animals: health, weight progress, size and more generally related information to the behavior of the cattle. For instance, during the food distribution, it is possible to monitor automatically with the help of cameras the whole cattle and identify animals not eating. The PhD candidate will mainly work on segmentation and classification problems using machine-learning techniques such as neural networks. He/she will publish obtained results in indexed international scientific journals and/or conferences.
This thesis is part of a collaborative project between ISEN-Lille and Gènes Diffusion Company. Selected candidate will benefit from a multidisciplinary expertise namely; i) 2D/3D image processing and analysis using artificial intelligence within ISEN-Lille, ii) generating/interpreting biologic data and development of bio-informatic analytic tools for predictive models in the field of animal production within Gènes Diffusion and iii) technology deployment for bio-species study within IEMN.
(c) GdR 720 ISIS - CNRS - 2011-2019.