Advances in seed phenotyping using X-ray imaging

Publication date: 25/07/2022
Extent: 22 pages

Contributions by: Sherif Hamdy, Aurélie Charrier and Laurence Le Corre, GEVES, France; Pejman Rasti, Université d’Angers and École d’ingénieur Informatique et Environnement (ESAIP), France; and David Rousseau, Université d’Angers, France

Chapter synopsis: In this chapter, we propose a panel of the imaging techniques dedicated to seed phenotyping. While review articles exist for most of these imaging techniques, X-Ray imaging, which can be considered as the most important one, still lacks of a synthetic overview. This is the focus on X-Ray imaging for seed that we propose in this chapter. We detail the workflow for this imaging modality and indicate the main parameters of importance to produce optimal image and extract the information they carry. We discuss the current literature on the topic and provide perspective for a wider dissemination in seed technologies. The chapter also provides some information on future trends in the direction of machine learning and gives links to additional material for video tutorials and datasets.

DOI: 10.19103/AS.2022.0105.08