Bayesian optimization for seed germination.

ABSTRACT:

Background

Efficient seed germination is a crucial task at the beginning of crop cultivation. Although boundaries of environmental parameters that should be maintained are well studied, fine-tuning can significantly improve the efficiency, which is infeasible to be done manually due to the high dimensionality of the parameter space.

Results

Traditionally seed germination is performed in climatic chambers with controlled environmental conditions. In this study, we perform a set of multiple-day seed germination experiments in the controllable environment. We use up to three climatic chambers to adjust humidity, temperature, water supply and apply machine learning algorithm called Bayesian optimization (BO) to find the parameters that improve seed germination. Experimental resu

SUBMITTER: Nikitin A 

PROVIDER: S-EPMC6487520 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature