Abstract
The article discusses the problems of crop yield forecasting. A review of relevant bibliographic sources has been carried out. Crop yield factors are analyzed. Climatic and meteorological features, production and agrotechnical parameters, soil moisture and fertilizers are considered as the main factors. It is noted that a full-scale field experimental study of crop yields requires very large time and financial costs. A conclusion was made about the issues related to modeling of product forecasting. A neural network for predicting crop yields (in case of winter wheat) was built by using the Python PyTorch machine learning library.
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