MODEL BUILDING AND FORECASTING USING MACHINE LEARNING ALGORITHMS FOR AGRICULTURAL PROBLEMS

Authors

  • Ch.B. Sabitov Kyrgyz National University named of Balasagyn Author
  • Z. Almasbekova Kyrgyz National University named of Balasagyn Author
  • А.К. Orozobekova Kyrgyz State University of Construction, Transport and Architecture named after N. Isanov Author
  • B.R. Sabitov Kyrgyz National University named of Balasagyn Author

Keywords:

Machine learning, algorithms, database, crops, forecasting, testing

Abstract

This article explores the process of building models based on machine learning algorithms. In recent years, depending on the use of various fertilizers (potassium, phosphorus, nitrogen, etc.), weather conditions (temperature, humidity, etc.), as well as deterioration of soil fertility, the preservation of crop yields for many farmers is a paramount task. The results of the analysis of various algorithms are obtained.

References

1. Орельен Жерон − Прикладное машинное обучение с помощью Scikit-Learn и TensorFlow, 2018 г.

2. Гудфеллоу Я., Бенджио И., Курвилль А. − Глубокое обучение, 2017 г.

3. Дж. Вандер Плас – Python для сложных задач. Наука о данных и машинное обучение,2020.

4. Ричард Саттон, Эндрю Барто − Обучение с подкреплением, 2017 г.

5. Андрей Бурков − The Hundred-Page Machine Learning Book, 2019 г.

6. Максим Лапань − Deep Reinforcement Learning Hands-On, 2018 г.

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Published

2026-03-03

Issue

Vol. 45 No. 3 (2021): СОВРЕМЕННЫЕ ПРОБЛЕМЫ МЕХАНИКИ

Section

Статьи

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