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Søren Kelstrup Skovsen


Søren Kelstrup Skovsen
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Project title:  AI-assisted inspection of clover grass fields based on deep learning for targeted fertilisation

This Ph.D project is supported by the Innovation Fund project SmartGrass and the GUDP project CloverSense. The common goal of the projects is to develop the use of precision agriculture for clover grass fields. Adapting the fertilisation strategies to the local conditions of each field leads to higher quality yields while reducing the impact on the environment.

The aim of the Ph.D project is to develop the method for analysing the local conditions of the clover grass fields. Through the use of machine learning, gathered images of the field are to be transformed into accurate measures, such as yield estimates and clover to grass ratios, without human interaction.

The main focus of the research lies within the field of vision-based Deep Learning - specifically within the topics of semi-supervised learning, semantic segmentation and instance segmentation. To exemplify, this can allow the computer to translate a simple image into a precise map of objects and plant species present in the image.

Main supervisor: Professor (Docent) Henrik Karstoft

Co-supervisor: Senior Researcher Rasmus Nyholm Jørgensen

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