Long-term frost forecasting using machine learning methods in a precision farming system

Aleksandr Molin; Olga Mitrofanova; Vladimir Bure; Evgeniy Mitrofanov

doi:10.55186/25876740_2025_68_7_958

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LONG-TERM FROST FORECASTING USING MACHINE LEARNING METHODS IN A PRECISION FARMING SYSTEM Long-term frost forecasting using machine learning methods in a precision farming system

АА Аа

Published in INTERNATIONAL AGRICULTURAL JOURNAL · Pages 958–962 · Rubric: Scientific support and management of agrarian and industrial complex

DOI: https://doi.org/10.55186/25876740_2025_68_7_958

Received: 13.09.2025 Accepted: 15.10.2025 Published: 15.12.2025

Authors

Aleksandr Molin

ORCID

Olga Mitrofanova

ORCID

Vladimir Bure

ORCID

Evgeniy Mitrofanov

ORCID

Aleksandr Molin

Academic degree: candidate of technical sciences

Saint Petersburg State University junior researcher · Russian Federation · Saint-Petersburg

ORCID

Olga Mitrofanova

Academic degree: candidate of technical sciences

Science rang: docent

Saint Petersburg State University associate professor of the department; senior researcher · department of programming technologies · Russian Federation · Saint-Petersburg

ORCID

Vladimir Bure

Academic degree: doctor of technical sciences

Science rang: professor

Saint Petersburg State University professor of the department; leading researcher · Department of Mathematical Game Theory and Statistical Decision Making · Russian Federation · Saint-Petersburg

ORCID

Evgeniy Mitrofanov

Academic degree: candidate of technical sciences

Science rang: docent

Saint Petersburg State University associate professor of the department; senior researcher · department of programming technologies · Russian Federation · Saint-Petersburg

ORCID

ABSTRACT

The purpose of the study was to train and compare machine learning (ML) and deep learning (DL) models for long-term forecasting of minimum daily temperatures (frosts) - a key agrometeorological risk affecting crop productivity. The study was based on data from weather stations in St. Petersburg and the Leningrad region. To predict the year ahead, which aligns with the needs of operational agricultural production planning (sowing dates, harvest timing, protective measures), 8 methods were used: ForecasterAutoreg, Random Forest, Support Vector Regression (SVR), XGBoost, Convolutional Neural Network (CNN), SimpleRNN, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). The analysis was carried out on two datasets: for the periods 1936-2024 and 1881-1995. The quality of the models was assessed using the metrics MAE, MSE, RMSE, R2 and adjusted R2. The LSTM model showed the most accurate results on the main dataset (1936-2024): MAE 2.9, MSE 14.661, R2 0.789. The SVR method (MAE 3.461, R2 0.775) demonstrated the best metrics based on archived data (1881-1995). It has been established that DL models (LSTM, GRU, CNN) generally outperform classical ML methods based on modern data. The LSTM method is recognized as the most effective for integration into precision farming systems and agricultural monitoring in the region for the advance planning of agrotechnological measures to protect crops from frost, optimization of crop rotation systems, and minimization of crop loss risks.

KEYWORDS

long-term agrometeorological forecasting, freezing, machine learning methods, deep learning methods, precision farming, agricultural management, decision-making in crop production

Funding

The study was supported by the Russian Science Foundation grant No. 24-21-00231, https://rscf.ru/en/project/24-21-00231.

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Editor-in-Chief

Aleksandr Fomin

Deputy Editor-in-Chief

Tat'yana V. Kazennova

Responsible Secretary

Irina Mamontova

Issue Editor

Gilyana Yakushkina

Literary Editor

Marina G. Fomina

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GOST

Molin A. E., Mitrofanova O. A., Bure V. M., Mitrofanov E. P. Long-term frost forecasting using machine learning methods in a precision farming system // INTERNATIONAL AGRICULTURAL JOURNAL. 2025. no. 7. pp. 958-962. DOI: https://doi.org/10.55186/25876740_2025_68_7_958. (Date of access 13.09.2026).

MLA

Aleksandr Molin, Mitrofanova O. A., Bure V. M., Mitrofanov E. P.. "Long-term frost forecasting using machine learning methods in a precision farming system." INTERNATIONAL AGRICULTURAL JOURNAL.7 (2025): 958-962. DOI: https://doi.org/10.55186/25876740_2025_68_7_958.

APA

Molin A. E., Mitrofanova O. A., Bure V. M., Mitrofanov E. P. (2025). Long-term frost forecasting using machine learning methods in a precision farming system. INTERNATIONAL AGRICULTURAL JOURNAL(7), 958-962. https://doi.org/10.55186/25876740_2025_68_7_958

Chicago

Aleksandr Molin, Mitrofanova O. A., Bure V. M., Mitrofanov E. P.. "Long-term frost forecasting using machine learning methods in a precision farming system." INTERNATIONAL AGRICULTURAL JOURNAL, no. 7 (2025): 958-962. https://doi.org/10.55186/25876740_2025_68_7_958.

WIKI

{{cite journal | author = Molin A. E., Mitrofanova O. A., Bure V. M., Mitrofanov E. P. | title = Long-term frost forecasting using machine learning methods in a precision farming system | journal = INTERNATIONAL AGRICULTURAL JOURNAL | year = 2025 | issue = 7 | pages = 958-962 | doi = 10.55186/25876740_2025_68_7_958 }}

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LONG-TERM FROST FORECASTING USING MACHINE LEARNING METHODS IN A PRECISION FARMING SYSTEM Long-term frost forecasting using machine learning methods in a precision farming system

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