Development of an Occurrence Prediction Model for Cucumber Downy Mildew in Solar Greenhouses Based on Long Short-Term Memory Neural Network

文献类型： 外文期刊

第一作者： Liu, Kaige

作者： Liu, Kaige;Zhang, Chunhao;Yang, Xinting;Diao, Ming;Liu, Huiying;Li, Ming;Liu, Kaige;Zhang, Chunhao;Yang, Xinting;Li, Ming

作者机构：

关键词： Pseudoperonospora cubensis; greenhouse; fruit cucumber; forecast model; deep learning

期刊名称：AGRONOMY-BASEL （ 影响因子：3.949； 五年影响因子：4.117 ）

ISSN：

年卷期： 2022 年 12 卷 2 期

页码：

收录情况： SCI

摘要： The occurrence of cucumber downy mildew in solar greenhouses directly affects the yield and quality of cucumber. Chemical control methods may cause excessive pesticide residues, endanger food quality and safety, pollute the ecological environment, etc. Therefore, it is very important to predict the disease before its occurrence. To provide farmers with better and effective guidance for the prevention and control work, minimize the loss of disease damage, this article took cucumber 'Lyujingling No. 2 ' as the experimental material and acquired greenhouse environmental factors data by wireless sensors, including Temp (Temperature), RH (Relative Humidity), ST (Soil Temperature) and SR (Solar Radiation). LSTM (Long Short-Term Memory) neural network structure was constructed based on Keras deep learning framework to develop a prediction model with time-series environmental factors. Combined with the occurrence of downy mildew from manual investigation and statistics, through debugging the parameters, this article developed an occurrence prediction model for cucumber downy mildew and compared it with KNN (K-Nearest Neighbors Classification) and ANN (Artificial Neural Network). In the prediction model, the forecasted results of the four environmental factors were consistent with the true value distributions, and R-2 (R-Squared) were all above 0.95. Among them, the ST variable predicted the best results, e.g., R-2 = 0.9982, RMSE (Root Mean Square Error) = 0.08 degrees C, and MAE (Mean Absolute Error) = 0.05 degrees C. In the disease occurrence prediction model, the training accuracy was 95.99%, the Loss value was 0.0159, the disease occurrence prediction Accuracy was 90%, Precision was 94%, Recall was 89%, F-1-score was 91%, the AUC (Area Under Curve) value was 90.15%, and Kappa coefficient was 0.80. It also had obvious advantages over other different models. In summary, the model had a high classification accuracy and performance, and it can provide a reference for the occurrence prediction of cucumber downy mildew in actual production.

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