Articles
Monitoring, early warning and ecological control on cucumber downy mildew in Chinese solar greenhouses
Article number
1411_11
Pages
105 – 110
Language
English
Abstract
Cucumber downy mildew is caused by Pseudoperonospora cubensis and threatens sustainable production.
To reduce the fungicide amount and support integrated pest management, this paper introduces a solution on monitoring, early warning and ecological control on cucumber downy mildew in Chinese solar greenhouses, 1) the pathogen detection and monitoring method were established based on laser technology, photoelectric measurement technology, computer image technology, fluid mechanics and cellular immunofluorescence chemistry technology; 2) under facility conditions, we studied the interaction system between pathogens, crops, soil, etc., and use chlorophyll fluorescence imaging systems to develop the early detection method for crop diseases; 3) integrated the data from disease tetrahedron including pathogens, crops, outside weather plus inside microclimate and cultivation, we analyzed the different epidemic laws and establish prediction models using statistics and deep learning method; 4) according to the ecological control, a selective temperature control strategy with a humidity priority control scheme was proposed for greenhouse microclimate optimization to reduce the disease occurrence risk; 5) finally, an epidemic monitoring and warning system was developed combined with intelligent monitoring equipment and prediction models.
This concept could be applied in Cucurbitaceae and other crops to accelerate the process of intelligent agriculture in China.
To reduce the fungicide amount and support integrated pest management, this paper introduces a solution on monitoring, early warning and ecological control on cucumber downy mildew in Chinese solar greenhouses, 1) the pathogen detection and monitoring method were established based on laser technology, photoelectric measurement technology, computer image technology, fluid mechanics and cellular immunofluorescence chemistry technology; 2) under facility conditions, we studied the interaction system between pathogens, crops, soil, etc., and use chlorophyll fluorescence imaging systems to develop the early detection method for crop diseases; 3) integrated the data from disease tetrahedron including pathogens, crops, outside weather plus inside microclimate and cultivation, we analyzed the different epidemic laws and establish prediction models using statistics and deep learning method; 4) according to the ecological control, a selective temperature control strategy with a humidity priority control scheme was proposed for greenhouse microclimate optimization to reduce the disease occurrence risk; 5) finally, an epidemic monitoring and warning system was developed combined with intelligent monitoring equipment and prediction models.
This concept could be applied in Cucurbitaceae and other crops to accelerate the process of intelligent agriculture in China.
Publication
Authors
Ming Li, Ran Liu, Xiaohui Chen, Kaige Liu, Chunhao Zhang, Baoyu Hao, Dongyuan Shi, Xinting Yang
Keywords
smart agriculture, artificial intelligence, biotic stresses, fruit quality safety, protected cultivation
Groups involved
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