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An Artificial Neural Network Model to Predict the Phenology of Early-Maturing Soybean Varieties from Climatic Factors

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Abstract—Soybean phenology is strongly influenced by temperature and day length, and phenological records clearly reflect the changes in climatic conditions. A model including three artificial neural networks was designed to predict the time intervals between sowing, emergence, flowering, and maturity as dependent on climatic factors. Ensemble regression models were constructed to predict the yield, seed protein, and oil content in soybean. Data on maturation were analyzed for early-maturing soybean accessions phenotyped at two experimental stations of Vavilov Institute of Plant Genetic Resources in the North-Caucasian and Northwestern regions of Russia. The model was implemented in Python using the Keras and TensorFlow packages.

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ACKNOWLEDGMENTS

Computations were performed at the Polytechnic Supercomputer Center of St. Petersburg State University and a cluster of University of Southern California.

Initial data were obtained on the basis of the unique research tool Collection of Plant Genetic Resources (Vavilov Institute of Plant Genetic Resources).

Funding

This work was supported by the Federal Grant Program (project no. 14.575.21.0136 dated September 26, 2017, unique project identifier RFMEFI57517X0136).

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    O. D. Taratuhin, L. Yu. Novikova, S. V. Nuzhdin, M. G. Samsonova & K. N. Kozlov

  2. Vavilov All-Russian Institute of Plant Genetic Resources, 190000, St. Petersburg, Russia

    L. Yu. Novikova, I. V. Seferova & T. V. Gerasimova

  3. University of Southern California, 90089, Los Angeles, CA, United States

    S. V. Nuzhdin

Authors
  1. O. D. Taratuhin
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  2. L. Yu. Novikova
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  3. I. V. Seferova
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  4. T. V. Gerasimova
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  5. S. V. Nuzhdin
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  6. M. G. Samsonova
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  7. K. N. Kozlov
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Corresponding author

Correspondence to K. N. Kozlov.

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Conflict of interests. The authors declare that they have no conflict of interest. This article does not contain any studies involving animals or human subjects performed by any of the authors.

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Translated by T. Tkacheva

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Taratuhin, O.D., Novikova, L.Y., Seferova, I.V. et al. An Artificial Neural Network Model to Predict the Phenology of Early-Maturing Soybean Varieties from Climatic Factors. BIOPHYSICS 65, 106–117 (2020). https://doi.org/10.1134/S0006350920010200

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  • DOI: https://doi.org/10.1134/S0006350920010200

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