Code
DOCT/006259
Duration
26 October 2023 → 21 September 2025 (Ongoing)
Doctoral researcher
Research disciplines
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Natural sciences
- Machine learning and decision making
- Computer vision
- Remote sensing
-
Agricultural and food sciences
- Agricultural technology
Keywords
machine learning and remote sensing
Agroecology
Self-supervised learning
Project description
The goal of this doctorate is to develop a method to detect and identify weeds in an agricultural context, which will be done with high-resolution RGB and multispectral UAV imagery. An additional goal is to only rely on automatic and unsupervised/self-supervised machine learning models. This doctorate is part of the Europe Horizon project “Agroecology for weeds” (GOOD).