We use some essential cookies to make this website work.
We’d like to set additional cookies to understand how you use forestresearch.gov.uk, remember your settings and improve our services.
We also use cookies set by other sites to help us deliver content from their services.
Tree seeds vary in size and shape, which influences seed processing efficiency and impacts on seed lot quality. In some species, seed lots contain high proportions of empty seeds and cannot be single sown in container nurseries. Nowadays single-seed processing is possible with seed phenotyping, which generates metrics from images produced across the electromagnetic spectrum. Seed phenotyping is well-suited to automation, which potentially enables real-time assessments of seed lot quality. This research aims to develop software that can use image metrics to automatically differentiate between seed categories (filled, empty or insect-infested) from x-ray images.
The research objective is to develop software that can use image metrics to automatically differentiate between seed categories. The main aims are to:
Using R-software, three functions have been developed to automatically predict seed category. The first function processes raw images by removing background objects, re-aligning seeds, and standardizing image intensity with a fixed calibration reference. The second function extracts relevant image metrics including line profiles and texture for each seed. The third function tests the attributes of loaded seeds against a library of pre-identified seeds. Using hard validation, our models had a high degree of accuracy and precision, which shows potential for further development with a wider range of species.
Statistician
Seed Technician
Cookies are files saved on your phone, tablet or computer when you visit a website.
We use cookies to store information about how you use the dwi.gov.uk website, such as the pages you visit.
Find out more about cookies on forestresearch.gov.uk
We use 3 types of cookie. You can choose which cookies you're happy for us to use.
These essential cookies do things like remember your progress through a form. They always need to be on.
We use Google Analytics to measure how you use the website so we can improve it based on user needs. Google Analytics sets cookies that store anonymised information about: how you got to the site the pages you visit on forestresearch.gov.uk and how long you spend on each page what you click on while you're visiting the site
Some forestresearch.gov.uk pages may contain content from other sites, like YouTube or Flickr, which may set their own cookies. These sites are sometimes called ‘third party’ services. This tells us how many people are seeing the content and whether it’s useful.