OCT imaging of plant’s early infection

Grantham Scholar Ghada Sasi is using novel laser-based imaging techniques to detect early infection of crops.

Background to the project

Septoria tritici blotch STB (also known as yellow leaf spot) is a disease caused by fungus and one of the most devastating foliar diseases of wheat in the European Union (Fones and Gurr 2015) and causes major damage to the UK winter wheat (Shaw and Royle 2007). Hence, to maximise the survival of infected crops and to minimize the use of chemical treatment, and thus minimize the treatment’s environmental impact, one avenue is to detect the onset of infection as early as possible.

Objectives of the project

In order to detect early crops infection, we propose to use Optical Coherence Tomography (OCT) as a novel imaging tool to provide cross-sectional and three-dimensional non-invasive in vivo monitoring of plant microstructure in real-time. Using this technique, we hope to detect spores before the crop shows any visible signs of infection and learn more about the different infection stages. Once the technique passes the proof-of-concept stage, the goal is to evaluate its use in the field.

Methodology of the project

In my PhD research, I am focusing on OCT as a useful tool to detect Septoria fungus (Septoria tritici blotch STB) that survives on wheat crops in early stages when the humidity is high (Damp and moist conditions are typical for the UK). The first step of the project is to acquire series of OCT images after infection and monitor the spreading of the fungus. This data, along with complementary confocal images will teach us about infection mechanisms.

The second step is to compare such infection time-lap with hyperspectral imaging, which is currently used to monitor crops’ health. Through such comparison, we can find what OCT resolution is required to equates the current imaging alternatives in detecting crop infection.

Fina Ghada Sasi on social media

You can find Ghada on LinkedIn.

Research groups

Ghada is part of Dr. Adrien Chauvet’s research group.

Supervisor

Co-Supervisors

Professor Stephen Matcher

Department of Electronic and Electrical Engineering