文献类型: 外文期刊
作者: Yang, Xinting 1 ; Li, Ming 2 ; Zhao, Chunhang 2 ; Zhang, Zheng 2 ; Hou, Yanlin 3 ;
作者机构: 1.Chinese Acad Sci, Ecoenvironm Sci Res Ctr, Beijing 100085, Peoples R China
2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
3.Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China
关键词: cucumber downy mildew; early warning model; greenhouse; good agricultural practice (GAP); risk analysis
期刊名称:NEW ZEALAND JOURNAL OF AGRICULTURAL RESEARCH ( 影响因子:2.161; 五年影响因子:1.988 )
ISSN: 0028-8233
年卷期: 2007 年 50 卷 5 期
页码:
收录情况: SCI
摘要: An early warning model for occurrence of cucumber downy mildew in non-heated greenhouses was developed based on disease records and microclimatic parameter analysis. It also integrated empirical and fundamental forecasting models, early warning theory and plant protection product risk analysis. The thresholds for infection were a daily temperature range 5'C, daily mean RH > 80% and daily mean temperature at 15-25'C in autumn. The temperature contribution rate for symptom appearance was measured by the reciprocal of the days of the inoculation period and the relationship was fitted by a polynomial of degree 3. Data recorded in two greenhouses were used to construct the model and its predictions were evaluated with an independent dataset. The warning source search, warning obviation and plant protection production risk analysis were based on China's Good Agricultural Practices (CHINAGAP). The implications of the variables and modules of the model in the safety production are discussed.
- 相关文献
作者其他论文 更多>>
-
GCVC: Graph Convolution Vector Distribution Calibration for Fish Group Activity Recognition
作者:Zhao, Zhenxi;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Liu, Jintao
关键词:Fish; Feature extraction; Activity recognition; Calibration; Adhesives; Training; Convolution; Graph convolution vector calibration; fish group activity; activity feature vector calibration; fish activity dataset
-
Porphyrin fluorescence imaging for real-time monitoring and visualization of the freshness of beef stored at different temperatures
作者:Liu, Huan;Zhu, Lei;Ji, Zengtao;Zhang, Min;Yang, Xinting;Liu, Huan;Zhu, Lei;Ji, Zengtao;Yang, Xinting;Zhang, Min;Liu, Huan;Ji, Zengtao;Yang, Xinting;Liu, Huan;Ji, Zengtao;Yang, Xinting
关键词:Porphyrin; Fluorescence imaging; Beef; Freshness; Visualization
-
Integration of Deep Learning and Sparrow Search Algorithms to Optimize Greenhouse Microclimate Prediction for Seedling Environment Suitability
作者:Shi, Dongyuan;Yuan, Pan;Liang, Longwei;Li, Ming;Diao, Ming;Shi, Dongyuan;Li, Ming;Gao, Lutao
关键词:CNN; greenhouse microclimate; LSTM; sparrow search algorithm; time series prediction
-
Role of Rain in the Spore Dispersal of Fungal Pathogens Associated with Grapevine Trunk Diseases
作者:Ji, Tao;Ji, Tao;Altieri, Valeria;Salotti, Irene;Rossi, Vittorio;Li, Ming
关键词:Bayesian analysis; grapevine trunk diseases; rain threshold; spore sampling
-
Seasonal Periodicity of the Airborne Spores of Fungi Causing Grapevine Trunk Diseases: An Analysis of 247 Studies Published Worldwide
作者:Ji, Tao;Ji, Tao;Salotti, Irene;Altieri, Valeria;Rossi, Vittorio;Li, Ming
关键词:grapevine trunk diseases; inoculum presence; seasonal periodicity; spore trapping
-
FCFormer: fish density estimation and counting in recirculating aquaculture system
作者:Zhu, Kaijie;Ma, Pingchuan;Zhu, Kaijie;Yang, Xinting;Yang, Caiwei;Fu, Tingting;Ma, Pingchuan;Hu, Weichen;Zhu, Kaijie;Yang, Xinting;Yang, Caiwei;Fu, Tingting;Ma, Pingchuan;Hu, Weichen;Zhu, Kaijie;Yang, Xinting;Yang, Caiwei;Fu, Tingting;Ma, Pingchuan;Hu, Weichen
关键词:recirculating aquaculture systems; density estimation; fish counting; transformer; deep learning
-
Numerical Simulation of Structural Performance in a Single-Tube Frame for 12 m-Span Chinese Solar Greenhouses Subjected to Snow Loads
作者:Li, Ming;Zhao, Qingsong;Wei, Xiaoming;Wang, Lichun;Wei, Xiaoming;Wang, Lichun
关键词:reinforcement; initial geometry imperfection; cross-section



