| 北京地区秋季日光温室黄瓜白粉病预测的贝叶斯网络模型研究 |
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| 引用本文： | 魏少伟,任爱新,赵靖暄,杨信廷,李明,刘慧英.北京地区秋季日光温室黄瓜白粉病预测的贝叶斯网络模型研究[J].中国瓜菜,2022(2):20-27. |
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| 作者姓名： | 魏少伟 任爱新 赵靖暄 杨信廷 李明 刘慧英 |
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| 作者单位： | 特色果蔬栽培生理与种质资源利用兵团重点实验室·石河子大学农学院;农产品质量安全追溯技术及应用国家工程实验室·北京市农林科学院信息技术研究中心·国家农业信息化工程技术研究中心·中国气象局-农业农村部都市农业气象服务中心 |
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| 基金项目： | 北京市农林科学院农业科技示范推广项目(2020305、2020306);国家自然科学基金青年科学基金项目(31401683);国家重点研发计划政府间国际科技创新合作重点专项(2017YFE0122503)。 |
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| 摘 要： | 为了准确预测日光温室黄瓜白粉病的发生,以日光温室水果黄瓜为试验材料,于2020年9—11月在北京市不同方位的4个日光温室内,运用无线网络环境监测系统对日光温室黄瓜的生长环境(空气温度、相对湿度、光照强度)进行了实时动态监测,并同步进行白粉病流行调查.采用贝叶斯网络模型建立日光温室黄瓜白粉病预测模型,预测黄瓜白粉病是否发...
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| 关 键 词： | 黄瓜 白粉病 贝叶斯网络 预测模型 |
Prediction of cucumber powdery mildew in autumn solar greenhouse in Beijing based on Bayesian network model |
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| WEI Shaowei,REN Aixin,ZHAO Jingxuan,YANG Xinting,LI Ming,LIU Huiying.Prediction of cucumber powdery mildew in autumn solar greenhouse in Beijing based on Bayesian network model[J].China Cucurbits And Vegetables,2022(2):20-27. |
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| Authors: | WEI Shaowei REN Aixin ZHAO Jingxuan YANG Xinting LI Ming LIU Huiying |
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| Affiliation: | (Key Laboratory of Cultivation Physiology and Germplasm Resources Utilization of Featured Fruits and Vegetables of Xinjiang Production and Construction Corps/College of Agriculture,Shihezi University,Shihezi 832003,Xinjiang,China;National Engineering Laboratory for Quality and Safety Traceability Technology and Application of Agricultural Products/Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences/National Engineering Research Center of Information Technology in Agriculture/Meteorological Service Center of Urban Agriculture,China Meteorological Administration-Ministry of Agriculture and Rural Affairs,Beijing 100097,China) |
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| Abstract: | To accurately predict the occurrence of cucumber powdery mildew in solar greenhouse, four solar greenhouses were chosen in Beijing from September to November 2020, using the wireless network environmental monitoring system and real-time dynamic monitoring to collect cucumber growth environment(air temperature, humidity, light intensity)and powdery mildew data. A forecast model of cucumber powdery mildew in solar greenhouse was developed using the Bayesian network model, the incidence of cucumber powdery mildew predicted was and compared with field observation.The model performed well in four greenhouses with the accuracy ACC= 0.95, 0.92, 0.91, 0.87, 0.87, and the Youden Index J=0.90, 0.86, 0.84, 0.70, 0.74;The results showed that the model had a good prediction at all greenhouse and each greenhouse level, and could provide a guide for powdery mildew management in the actual production. |
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| Keywords: | Cucumber Powdery mildew Bayesian network Prediction model |
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