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A model-based methodology for the early detection of downy mildew

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This study introduces a novel approach combining a mechanistic greenhouse climate model and a disease model for the prediction of disease occurrence in greenhouses. The method was evaluated in the National Precision Agriculture Demonstration Base (NPADB), Beijing, China, using data collected from transplanting to primary infection that occurred in the greenhouse, in the spring of 2021.

First, the dynamic model is used to predict the indoor climate of the greenhouse 72 h in advance. Then, this prediction is used as an input to the disease model to detect disease onset in advance. Predictions for greenhouse late blight were compared using real-time measured data for two months.

After several reports of false positives, a positive report by both methods corresponded to the first observation in the greenhouse on April 24, 2021. Thus, the main contribution of this work is the early warning of cucumber blight via climate and climate coupling models. disease, where only transient inputs from weather forecasts are needed.

Read the full research at www.researchgate.net.

Liu, Ran & Wang, Hui & Guzman, Jose & Li, Ming. (2022). A model-based methodology for the early detection of cucumber late blight in greenhouses: an experimental evaluation. Computers and electronics in agriculture. 10.1016/j.compag.2022.106751.