Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity.

ABSTRACT: Advances in optical imaging technology using rapid and non-destructive methods have led to improvements in the efficiency of seed quality detection. Accurately timing the harvest is crucial for maximizing the yield of higher-quality Siberian wild rye seeds by minimizing excessive shattering during harvesting. This research applied integrated optical imaging techniques and machine learning algorithms to develop different models for classifying Siberian wild rye seeds based on different maturity stages and grain positions. The multi-source fusion of morphological, multispectral, and autofluorescence data provided more comprehensive information but also increases the performance requirements of the equipment. Therefore, we employed three filtering algorithms, namely minimal joint mutual infor

SUBMITTER: Jia Z 

PROVIDER: S-EPMC10157248 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature