Plant Phenotyping as a multi task machine learning problem

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Engineering


Agricultural crop production is at risk for not keeping up with the growing demand given by world population growth and waste. To meet this expanding demand, plant breeders are focusing on finding more efficient plants which need to be grown in increasingly unfavourable environments as a consequence of climate change (Awlia et al., 2016). Plant phenotyping is a crucial step in this process as genetic variations need to be evaluated in terms of their phenotypic changes.
Machine learning is a powerful tool when trying to solve phenotyping problems. It allows scientists to utilize larger and more detailed datasets to discover patterns and drive discovery by measuring a combination of properties instead of analysing each one individually. Applications for plant phenotyping based on machine learning techniques have been made to segment plants (Minervini et al., 2014) as well as count leaves (Giuffrida, 2015).
My project will focus on implementing novel machine learning approaches and algorithms to solve current plant phenotyping problems. I plan to use multi-task deep learning and instance segmentation to detect and quantify different phenotypes such as leaf and plant growth as well as development. The objective is to build a robust system of object detection and tracking that could be used to phenotype multiple model plants such as Arabidopsis thaliana or Nicotiana tabacum.
Although plant phenotyping is an application in this project, the developed code and theory would be applicable to multiple other computer vision problems such as improved object detection and recognition for robotics or autonomous cars as they also must solve often complex and heavily occluded images.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509644/1 01/10/2016 30/09/2021
1785349 Studentship EP/N509644/1 01/10/2016 31/08/2020 Andrei Dobrescu