Cucumber Downy Mildew Disease Prediction Using a CNN-LSTM Approach

Author:

Wang Yafei12,Li Tiezhu1,Chen Tianhua3,Zhang Xiaodong1,Taha Mohamed Farag14ORCID,Yang Ning5,Mao Hanping1,Shi Qiang6

Affiliation:

1. School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

2. Jiangsu Changdian Technology Co., Ltd., Jiangyin 214400, China

3. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China

4. Department of Soil and Water Sciences, Faculty of Environmental Agricultural Sciences, Arish University, North Sinai 45516, Egypt

5. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

6. School of Science and Technology, Shanghai Open University, Shanghai 200433, China

Abstract

It is of great significance to develop early prediction technology for controlling downy mildew and promoting cucumber production. In this study, a cucumber downy mildew prediction method was proposed by fusing quantitative disease information and environmental data. Firstly, the number of cucumber downy mildew spores during the experiment was collected by a portable spore catcher, and the proportion of cucumber downy mildew leaf area to all cucumber leaf area was recorded, which was used as the incidence degree of cucumber plants. The environmental data in the greenhouse were monitored and recorded by the weather station in the greenhouse. Environmental data outside the greenhouse were monitored and recorded by a weather station in front of the greenhouse. Then, the influencing factors of cucumber downy mildew were analyzed based on the Pearson correlation coefficient method. The influencing factors of the cucumber downy mildew early warning model in greenhouse were identified. Finally, the CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) algorithm was used to establish the cucumber downy mildew incidence prediction model. The results showed that the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and determination coefficient (R2) of the CNN-LSTM network model were 0.069, 0.0098, 0.0991, and 0.9127, respectively. The maximum error between the predicted value and the true value for all test sets was 16.9398%. The minimum error between the predicted value and the true value for all test sets was 0.3413%. The average error between the predicted and true values for all test sets was 6.6478%. The Bland–Altman method was used to analyze the predicted and true values of the test set, and 95.65% of the test set data numbers were within the 95% consistency interval. This work can serve as a foundation for the creation of early prediction models of greenhouse crop airborne diseases.

Funder

the National Natural Science Foundation of China

Priority Academic Program Development of Jiangsu Higher Education Institutions

National Key Research and Development Program

Major Science and Technology Project of Xinjiang Uygur autonomous region

Project of Agricultural Equipment Department of Jiangsu University

Key Laboratory of Modern Agricultural Equipment and Technology (Jiangsu University), Ministry of Education

National Key Research and Development Program for Young Scientists

Publisher

MDPI AG

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