Cargando…
Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model
Crop evapotranspiration estimation is a key parameter for achieving functional irrigation systems. However, ET is difficult to directly measure, so an ideal solution was to develop a simulation model to obtain ET. There are many ways to calculate ET, most of which use models based on the Penman–Mont...
Autores principales: | Ge, Jiankun, Zhao, Linfeng, Yu, Zihui, Liu, Huanhuan, Zhang, Lei, Gong, Xuewen, Sun, Huaiwei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9330426/ https://www.ncbi.nlm.nih.gov/pubmed/35893626 http://dx.doi.org/10.3390/plants11151923 |
Ejemplares similares
-
Performance of the Improved Priestley-Taylor Model for Simulating Evapotranspiration of Greenhouse Tomato at Different Growth Stages
por: Gong, Xuewen, et al.
Publicado: (2022) -
Evaluation of Irrigation Modes for Greenhouse Drip Irrigation Tomatoes Based on AquaCrop and DSSAT Models
por: Ge, Jiankun, et al.
Publicado: (2023) -
Root Distribution of Tomato Cultivated in Greenhouse under Different Ventilation and Water Conditions
por: Ge, Jiankun, et al.
Publicado: (2023) -
Development of Smart Weighing Lysimeter for Measuring Evapotranspiration and Developing Crop Coefficient for Greenhouse Chrysanthemum
por: Sagar, Atish, et al.
Publicado: (2022) -
Machine Learning-Based Crop Stress Detection in Greenhouses
por: Elvanidi, Angeliki, et al.
Publicado: (2022)