Abstract
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 R2 (R-Squared) were all above 0.95. Among them, the ST variable predicted the best results, e.g., R2 = 0.9982, RMSE (Root Mean Square Error) = 0.08 °C, and MAE (Mean Absolute Error) = 0.05 °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%, F1-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.
Highlights
Cucumber downy mildew is a devastating leaf disease caused by the oomycete Pseudoperonospora cubensis (Berk. & Curt.) Rostov., the pathogenic processes and epidemiology are closely related to environmental conditions [1]
According to the time-series environmental factors data acquired by the sensors, the missing data was processed through linear interpolation, and the four model independent variables were predicted including the temperature (Temp), relative humidity (RH), soil temperature (ST), and solar radiation (SR) in the greenhouse
This article developed a cucumber downy mildew prediction model based on the data of environmental factors in the solar greenhouse
Summary
Cucumber downy mildew is a devastating leaf disease caused by the oomycete Pseudoperonospora cubensis (Berk. & Curt.) Rostov., the pathogenic processes and epidemiology are closely related to environmental conditions [1]. Cucumber downy mildew is a devastating leaf disease caused by the oomycete Pseudoperonospora cubensis The climate environment in solar greenhouses is conducive to the dispersal of pathogens and sporangia infection, leading to the serious occurrence and rapid spread of cucumber downy mildew [2,3]. If it is not controlled in time, it will cause major production and economic losses [4]. Chemical agents are mainly used in production to prevent and control cucumber downy mildew, which contradicts the concept of green development, and causes excessive pesticide residues, endangers food quality and safety, and pollutes the ecological environment. Accurate and effective disease prediction is of great significance to the meticulous management, intelligent decisionmaking of cucumbers, and sustainable development of the agro-ecosystem
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