kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2026-23-2-290-311kaz29-2912Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEПРОГНОЗИРОВАНИЕ УРОЖАЙНОСТИ РИСА С ИСПОЛЬЗОВАНИЕМ МАШИННОГО ОБУЧЕНИЯ: СРАВНИТЕЛЬНЫЙ АНАЛИЗ БАЗОВЫХ И АНСАМБЛЕВЫХ МОДЕЛЕЙ НА ПРИМЕРЕ ОКРУГА УДХАМ-СИНГХ-НАГАР, ШТАТ УТТАРАКХАНД (ИНДИЯ)MACHINE LEARNING-DRIVEN PADDY YIELD PREDICTION: COMPARATIVE EVALUATION OF BASELINE VS. ENSEMBLE MODELS IN UDHAM SINGH NAGAR, UTTARAKHANDhttps://orcid.org/0000-0002-2367-0897КулялМ.KulyalM.

Кафедра компьютерных наук.

Алмора 263601, Уттаракханд

Department of Computer Science.

Almora-263601, Uttarakhand

malika_21@rocketmail.comhttps://orcid.org/0000-0002-9458-5817Уманг.Umang.

д-р. - Кафедра компьютерных приложений.

Уттаракханд

Dr. Department of Computer Applications.

Nainital, Uttarakhand

anilumang@yahoo.co.inhttps://orcid.org/0009-0007-0544-593XСаксенаП.SaxenaP.

д-р. - Кафедра компьютерных наук.

Алмора 263601, Уттаракханд

Dr. Department of Computer Science.

Almora-263601, Uttarakhand

parul_saxena@yahoo.comhttps://orcid.org/0000-0003-2279-5556ПантДж.PantJ.

д-р. - Кафедра компьютерных наук и инженерии.

Кампус Бхимтал, Уттаракханд

Dr. Department of Computer Science and Engineering.

Bhimtal Campus, Uttarakhand

geujay2020@gmail.comУниверситет Soban Singh JeenaИндияSoban Singh Jeena UniversityIndiaO Университет Kumaun, НайниталИндияKumaun UniversityIndiaУниверситет Graphic Era HillИндияGraphic Era Hill UniversityIndia202627062026232290311Copyright © Кулял М., Уманг .., Саксена П., Пант Д., 20262026Кулял М., Уманг .., Саксена П., Пант Д.Kulyal M., Umang .., Saxena P., Pant J.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2912

Рис является краеугольным камнем продовольственной безопасности в Индии, поддерживая миллионы средств к существованию и национальную экономику. Однако нестабильные климатические условия делают урожайность риса все более непредсказуемой. В данном исследовании разрабатывается система машинного обучения для прогнозирования урожайности риса в районе Удхам Сингх Нагар, Уттаракханд, путем интеграции данных о погоде, почве и севах. Среди базовых классификаторов CatBoost показал лучшие результаты с точностью 80,85% и ROC-AUC 0,90. Для дальнейшего повышения производительности Optuna-tuning модели CatBoost, XGBoost и LightGBM были объединены в гибридные комплекты. Классификатор взвешенного жесткого голосования, придающий больший вес CatBoost ([3,1,1]), достиг наивысшей точности – 97,37%, за ним следуют ансамбли Stacking (95,6%) и ансамбли мягкого голосования (до 96%). Эти результаты были подтверждены высокими баллами ROC-AUC. В целом исследование показывает, что тщательно оптимизированные ансамблевые модели могут значительно повысить точность прогнозирования урожайности, предоставляя практический инструмент для более точного и устойчивого рисового выращивания в климатически чувствительных регионах Индии.

Rice is a cornerstone of food security in India, supporting millions of livelihoods and the national economy. However, erratic climate patterns are making paddy yields increasingly unpredictable. This study develops a machine learning framework for rice yield prediction in Udham Singh Nagar district, Uttarakhand, by integrating weather, soil, and crop data. Among baseline classifiers, CatBoost performed best with 80.85% accuracy and a ROC-AUC of 0.90. To further enhance performance, Optuna-tuned CatBoost, XGBoost, and LightGBM models were combined into hybrid ensembles. The Weighted Hard Voting classifier, giving higher weight to CatBoost ([3,1,1]), achieved the highest accuracy of 97.37%, followed by Stacking (95.6%) and Soft Voting ensembles (up to 96%). These results were supported by strong ROC-AUC scores. Overall, the study shows that carefully optimized ensemble models can significantly improve yield prediction accuracy, offering a practical tool for more precise and sustainable rice farming in climate-sensitive regions of India.

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The authors declare that there are no conflicts of interest present.