Abstract
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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