06.07.2020 - 08.07.2020 – Angers, France

POSTPONED - Hackaton plant phenotyping with minicomputers and low-cost cameras

Hackaton

Due to the global COVID-19 outbreak, this event is postponed to postponed to July 2021, Anger, France

Workshop on practical aspects of plant phenotyping with inexpensive, yet robust, minicomputers coupled to small cameras

From system installation, network deploying, time lapse acquisition, image processing, machine learning and data analysis, each step will be illustrated with talks and hands-on demonstrations.

Target audience: tech-minded biologists, engineers in charge of phenotyping trials from academic or private companies

Location: IRHS, 42 rue Georges Morel, Beaucouzé, Pays de la loire, France 

Programme:

Day 1:

9 am-12 pm: Time lapse acquisition

1 pm-5 pm: Pre-Processing Time lapse image sequence

5 pm-7 pm: Launching of a time lapse over a network of minicomputers

 

Day 2:

9 am-12 pm: Course Machine learning for plant growth analysis

1 pm-5 pm: Hands on machine learning for plant growth analysis

5 pm-7 pm: Show case of industrial and home-made solutions

 

Day 3:

8 am-12 pm: Example of Plant research with Minicomputers and low-cost cameras

2 pm-4 pm: Visit of PHENOTIC platform

 

Invited speakers: Noah FAHLGREN (Danforth center, USA), Sotirios TSAFTARIS (Edinburg University,

UK), Sebastian SCHULTHEISS (Computomics, Germany), Barna KERESZTES (IMS Lab, CNRS,

France), Julia BUITINK (INRAE, Angers, France)

Organizers: David ROUSSEAU (Université d’Angers, France), Pejman RASTI (ESAIP, France)

Local team: Hadhami GARBOUGE, Mouad El ZINE ABIDINE, David PIERRE, Geoffroy COUASNET,

Mathis CORDIER, Julien GARNIER, Félix MERCIER, Chafik LEKOUARA

 

Registration fee: 300 Euros - This includes coffee breaks, lunch, gala dinner, access to minicomputers, GPU programming station for machine learning, teaching material

 Registration deadline: 25 June 2020

Register Now

Payments must be made before 26 June 2020 by orderform followed by banktransfer at missions-irhs@inrae.fr


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EPPN2020 has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731013