Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature.

ABSTRACT:

Background

Rice is a major staple food crop for more than half the world's population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid an

SUBMITTER: Wang C 

PROVIDER: S-EPMC8783510 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature