文献类型： 外文期刊
作者： Liu, Ran 1 ; Liu, Jian 1 ; Liu, Huiying 3 ; Yang, Xinting 1 ; Bienvenido Barcena, Jose Fernando 2 ; Li, Ming 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Minist Agr,Informat Technol Res Ctr,Natl Engn Res, China Meteorol Adm,Minist Agr & Rural Affairs,Key, Meteorol Serv Ctr Urban Agr,Natl Engn Lab Agripro, Beijing, Peoples R China
2.Univ Almeria, Dept Informat, CIESOL, CeiA3, Ctra Sacramento S-N, Almeria, Spain
3.Shihezi Univ, Coll Agr, Key Lab Special Fruits & Vegetables Cultivat Phys, Shihezi, Xinjiang, Peoples R China
关键词： Computational fluid dynamics; Greenhouse; Cucumber; Canopy; Condensation
期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：4.123； 五年影响因子：4.508 ）
ISSN： 1537-5110
年卷期： 2021 年 210 卷
页码：
收录情况： SCI
摘要： Leaf wetness duration (LWD) provides the necessary conditions for pathogen infection. Among them, dew condensation on the crop canopy due to high humidity in a greenhouse is a major cause of LWD formation. However, it would be costly to monitor the condensation of all the leaves in a greenhouse. A computational fluid dynamics model was studied for the spatial and temporal distribution of the indoor microclimate and leaf condensation in a single-slope Chinese solar greenhouse at night. Models were embedded to simplify the input parameters and enhance the practicality. Without compromising the performance of the model, the model inputs were reduced to five: outdoor solar radiation intensity, outdoor air temperature, outdoor relative humidity, outdoor average wind speed per hour, and soil temperature. The distributions of roof condensation and leaf condensation were simulated. Condensation always appeared first on the roof rather than on the leaves. The leaf condensation results were manually observed for comparison with the simulated results. Leaf condensation always occurred first in the area near the semi-transparent roof, both in the observations and the simulation. The LWD was simulated by considering the duration of the simulated leaf condensation at each point. The evaluation was conducted on 216 pairs of samples. The True Negative Rate (TNR), True Positive Rate (TPR), and Accuracy (ACC) were 1, 0.66, and 0.89, respectively. This paper can serve as a reference for an early warning model of disease based on the temporal and spatial distribution of leaf condensation. (c) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.
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