This study introduces a new approach combining a mechanistic greenhouse climate model and a disease model for the forecast of diseases occurrence in greenhouses. The method was evaluated in NPADB (National Precision Agriculture Demonstration Base), Beijing, China using data collected from transplanting to the primary infection that occurred in the greenhouse, in the spring season of 2021.
First, the dynamic model is used to predict the greenhouse indoor climate 72 h ahead. Then, this prediction is used as input to the disease model to detect disease occurrence in advance. The predictions for the greenhouse downy mildew were compared using real-time measured data for two months.
After several false-positive reports, one positive report by both methods fitted the first observation in the greenhouse on April 24, 2021. Thus, the main contribution of this work is the early warning cucumber downy mildew via coupling climate and disease models, where only transient inputs from weather forecasts are required.
Read the complete research at www.researchgate.net.
Liu, Ran & Wang, Hui & Guzmán, José & Li, Ming. (2022). A model-based methodology for the early warning detection of cucumber downy mildew in greenhouses: An experimental evaluation. Computers and Electronics in Agriculture. 10.1016/j.compag.2022.106751.
