Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops.

ABSTRACT:

Background

Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments.

Results

We generated labeled imaging data of the germination process of more than 2400 seeds for three different crops, Zea mays (maize), Secale cereale (rye) and Pennisetum glaucum

SUBMITTER: Genze N 

PROVIDER: S-EPMC7754596 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature