A 3-D simulation of leaf condensation on cucumber canopy in a solar greenhouse

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.

Automated summary

A computational fluid dynamics model was used to study the spatial and temporal distribution of leaf wetness duration in a single-slope Chinese solar greenhouse. The model inputs were simplified to five parameters, and simulations showed the initial condensation always appeared on the roof before the leaves. The evaluation of the model showed high accuracy and can be used as a reference for an early warning disease model based on leaf condensation.