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Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes.

Jorge Marques da Silva¹, Andreia Figueiredo¹, Jorge Cunha²

¹Biosystems and Integrative Sciences Institute (BioISI), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.

Plants (Basel, Switzerland)

|February 7, 2020

Summary

This summary is machine-generated.

Machine learning algorithms can classify plant genotypes using rapid fluorescence transients. Genetic programming and neural networks show high success rates for identifying Vitis species and cultivars.

Keywords:

Kautsky effect Vitis artificial neural networks chlorophyll a fluorescence decision trees genetic programming k-nearest neighbors molecular markers photosynthesis

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Area of Science:

Plant physiology
Molecular biology
Computational biology

Background:

The Kautsky effect, a rapid polyphasic rise in fluorescence, reflects photochemical apparatus organization.
This organization is influenced by genotype-environment interactions.
Classifying plant genotypes is crucial for agriculture and research.

Purpose of the Study:

To evaluate machine learning techniques for classifying plant genotypes using rapid fluorescence transients.
To compare the performance of different machine learning algorithms in this classification task.
To assess the feasibility of using fluorescence transients for rapid Vitis genotype classification.

Main Methods:

Recording rapid fluorescence induction curves (Kautsky effect) in different Vitis species and cultivars.
Applying machine learning algorithms: k-nearest neighbors, decision trees, artificial neural networks, and genetic programming.
Establishing phylogenetic relations using molecular markers.

Main Results:

Genetic programming (75.3%) and neural networks (71.8%) achieved higher classification success rates than k-nearest neighbors (58.5%) or decision trees (51.6%).
All tested algorithms significantly outperformed random classification (14% success rate).
Genetic programming demonstrated slightly superior performance compared to neural networks.

Conclusions:

Rapid fluorescence transients, analyzed with machine learning, are effective for classifying Vitis genotypes.
Genetic programming shows particular promise for rapid, preliminary classification of Vitis species and cultivars.
This approach offers a feasible method for high-throughput plant genotype identification.