Bayesian optimization for seed germination

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...

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Bibliographic Details
Published in:Plant methods 2019-04, Vol.15 (1), p.43-43, Article 43
Main Authors: Nikitin, Artyom, Fastovets, Ilia, Shadrin, Dmitrii, Pukalchik, Mariia, Oseledets, Ivan
Format: Article
Language:English
Subjects:
Agriculture
Algorithms
Analysis
Artificial intelligence
Bayesian analysis
Bayesian optimization
Cultivation
Data mining
Distance learning
Efficiency
Environmental conditions
Environmental quality
Experiments
Gaussian process
Germination
Humidity
International conferences
Learning algorithms
Machine learning
Methods
Optimization
Parameter identification
Precision farming
Seed germination
Seeds
Stability
Temperature
Test chambers
Water shortages
Water supply
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Summary: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. 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 results show that our approach allows to increase the germination efficiency for different types of seeds compared to the initial expert knowledge-based guess. Our experiments demonstrated that BO could help to identify the values of the controllable parameters that increase seed germination efficiency. The proposed methodology is model-free, and we argue that it may be useful for a variety of optimization problems in precision agriculture. Further experimental studies are required to investigate the effectiveness of our approach for different seed cultures and controlled parameters.
ISSN:1746-4811
1746-4811
DOI:10.1186/s13007-019-0422-z