I work as a data scientist in the digital production group and I am part of the DataDrive team. I am involved in several project and I am managing the computer vision coordination project (a SFF11 subprojet).
Data science contains a broad spectrum of activities and it is a challenging field, not only because it comes at the interface of computer science and math, but also because:
- There are a lot of available tools in the market.
- Working in data science involves having some domain knowledge (often different than our domain knowledge of comfort)
- The reproducibility and generalizability of created systems and models
My main function is to mitigate these difficulties in a research context by contributing to data and machine learning aspects in the projects I am contributing to. Here are few examples of the projects I am working on:
- Computer vision coordination project: enable and foster computer vision based projects at agroscope by supporting CV based projects and organizing regular meetings and workshops about the topic.
- Rumex detection using computer vision: in this project, I work on the modelling and the system architecture.
- Smart irrigation using dendrometers signals over LoraWAN: in this project, I work on signal and data analysis and on implementing the backend routine for automation.
- Rhythmicity as welfare indicators for ruminants: I develop algorithms (in the context of the DigiRhythm package) and analyze data.
Google Scholar: https://scholar.google.fr/citations?user=L97ZODwAAAAJ&hl=en
ORCID: https://orcid.org/0000-0003-1821-3234
Linkedin: https://www.linkedin.com/in/nasserha
DigiRhythm Libray: https://cran.r-project.org/web/packages/digiRhythm/index.html
Smart weed control: https://www.agroscope.admin.ch/agroscope/en/home/topics/economics-technology/smart-farming/smart-weed-control.html
