Cucumber Downy Mildew Disease Prediction Using a CNN-LSTM Approach

Yafei Wang, Tiezhu Li, Tianhua Chen, Xiaodong Zhang, Mohamed Farag Taha, Ning Yang, Hanping Mao () and Qiang Shi
Additional contact information
Yafei Wang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Tiezhu Li: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Tianhua Chen: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Xiaodong Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Mohamed Farag Taha: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Ning Yang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Hanping Mao: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Qiang Shi: School of Science and Technology, Shanghai Open University, Shanghai 200433, China

Agriculture, 2024, vol. 14, issue 7, 1-17

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 ( R 2 ) 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.

Keywords: greenhouse; cucumber downy mildew; CNN-LSTM; prediction model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/7/1155/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/7/1155/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:7:p:1155-:d:1435999

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().