Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model.

Ge, Jiankun; Zhao, Linfeng; Yu, Zihui; Liu, Huanhuan; Zhang, Lei; Gong, Xuewen; Sun, Huaiwei

Ge, Jiankun; Zhao, Linfeng; Yu, Zihui; Liu, Huanhuan; Zhang, Lei; Gong, Xuewen; Sun, Huaiwei.

Afiliação

Ge J; School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450000, China.
Zhao L; School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450000, China.
Yu Z; School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450000, China.
Liu H; School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450000, China.
Zhang L; School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450000, China.
Gong X; School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450000, China.
Sun H; School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430030, China.

Plants (Basel) ; 11(15)2022 Jul 25.

Article em En | MEDLINE | ID: mdl-35893626

Palavras-chave

XGBoost regression; drip irrigated tomato; evapotranspiration; machine learning; solar greenhouse

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Plants (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Suíça

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