A novel approach integrating multispectral imaging and machine learning to identify seed maturity and vigor in smooth bromegrass.

ABSTRACT: Smooth bromegrass (Bromus inermis) was adopted as experiment materials for identifying the seed maturity using a combination of multispectral imaging and machine learning. The trials were conducted to investigate the effects of three nitrogen application levels (0, 100 and 200 kg N ha^- 1, defined as CK, N1 and N2 respectively) and two spikelet grain positions: superior grain (SG) at the basal position and inferior grain (IG) at the upper position, on smooth bromegrass seeds. The germination characteristics of the seeds revealed that the variations in nitrogen application and grain positions significantly influenced seeds vigor. The seed vigor of increased gradually with their maturity, reaching a high level at 30 and 36 days after anthesis. A stacking ensemble learning approach

SUBMITTER: Ou C 

PROVIDER: S-EPMC11938725 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature