Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley.

ABSTRACT:

Background

Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley (Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present.

Results

In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the mul

SUBMITTER: Ling Y 

PROVIDER: S-EPMC10604417 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature