葡萄霜霉病预测模型的研究
摘要
葡萄霜霉病是危害我国葡萄生产最严重的病害之一,葡萄霜霉病的有效预测是葡萄生产管理过程的重中之重。本论文的目的是研究葡萄霜霉病预测的有效方法,并构建葡萄霜霉病预警系统。本试验对卵孢子萌发模型、人工神经网络在葡萄霜霉病预测中的应用、构建葡萄霜霉病预警系统以及过去三年葡萄霜霉病卵孢子萌发和病害流行情况进行了研究,并着重对2007年杨凌地区严重的霜霉病流行状况进行分析研究。得出如下结果:
(1)本实验通过对葡萄霜霉病卵孢子萌发进行研究,进一步验证卵孢子萌发模型具有很高的精确率,高达85.966%。所以,该模型能准确预测卵孢子萌发的时期,可以用来指导葡萄生产中霜霉病的防治。
(2)降雨期是葡萄霜霉病防治最关键的时期。在卵孢子萌发时期,降雨带来的高湿度有利于卵孢子的萌发和游动孢子的初侵染,并进一步导致霜霉病菌的初侵染的发生。霜霉病流行后,高温低湿,不利于霜霉病菌的生长繁殖,但降雨带来的低温高湿将致使霜霉病菌以指数级的速度生长繁殖,病害迅速流行。所以,在条件不足时,可以把降雨应作为霜霉病防治的主要监测因素。
(3)霜霉病防治要把握三个关键时期:卵孢子萌发期、霜霉病初侵染期及病害流行后的降雨期。卵孢子萌发期,利用霜霉病卵孢子萌发模型预报霜霉病菌卵孢子的萌发期,在卵孢子萌发后,释放出来的孢子囊或游动孢子相对卵孢子较容易被杀死。4月～7月均有卵孢子萌发期,但是在4月～5月,霜霉病菌卵孢子的萌发不会致使初侵染的发生,属于无效萌发;只有在6月～7月初的卵孢子的萌发才具备足够的环境条件促使初侵染的发生,属于有效萌发,这个时间段的卵孢子萌发期才是防治的关键时期。霜霉病初侵染期,该时期可通过准确预测的卵孢子萌发期及构成侵染的潜育期推算出,并采取化学防治将可大大减缓霜霉病的发展。病害流行后的降雨期,降雨造成的高湿度和较低的温度均有利于霜霉病的复侵染,采取化学防治措施可阻止霜霉病害的流行。
(4)人工神经网络运用于葡萄霜霉病流行的预测具有很高的精确率,使用最大误差法计算其精确率,高达93.554%。所以,使用人工神经网络构建葡萄霜霉病复侵染模型是可行的,而且该模型能准确预测霜霉病的流行趋势。
(5)在霜霉病卵孢子萌发模型和霜霉病复侵染模型的研究的基础上,使用VC++程序语言构建了葡萄霜霉病预警系统。
Grape downy mildew, one of the most serious diseases, is harmful to China's grape production. It's the most important event to effectively forecast downy mildew during grape production management. Purpose of this paper was getting an effective way to predict downy mildew, and building a Grape downy mildew early warning system. In this paper, the oospores germination model of plasmopara viticola, the application of artificial neural network in forecasting grape downy mildew, building the grape downy mildew early warning system, the situation of plasmopara viticola oospores germination and the disease prevalence in the past three years were studied in Yangling district, and the results showed:
The oospores germination model was accurate in forecasting Oospores germination, and the precision was 85.966 percent. So the model can accurately predict the oospores germination period, and it can be used to guide the control of downy mildew in the course of grape production management.
Rainfall period is the most critical period in the course of the control of downy mildew. Rainfall would lead to low temperature and high humidity in summer. The low temperature and high humidity would help to Oospores germination, the first infection and distribution of downy mildew. While we lack of information concerned, rainfall should be believed as the most important fact in prediction.
If we want to control the downy mildew well, we must consider the three crucial periods: the oospores germination period, the first infection of downy mildew period and the rainfall period.
The model of forecasting grape downy mildew, based on Artificial Neural Network, is precise and effective in forecasting downy mildew. And the precision was 93.554 percent. So, it's available to predict the trend of downy mildew by Artificial Neural Network model. The trend of downy mildew will be predicted effectively.
Based on the research of the oospores germination model and the grape downy mildew forecasting model, the Grape downy mildew early warning system was built by the computer program VC++.
(1)本实验通过对葡萄霜霉病卵孢子萌发进行研究,进一步验证卵孢子萌发模型具有很高的精确率,高达85.966%。所以,该模型能准确预测卵孢子萌发的时期,可以用来指导葡萄生产中霜霉病的防治。
(2)降雨期是葡萄霜霉病防治最关键的时期。在卵孢子萌发时期,降雨带来的高湿度有利于卵孢子的萌发和游动孢子的初侵染,并进一步导致霜霉病菌的初侵染的发生。霜霉病流行后,高温低湿,不利于霜霉病菌的生长繁殖,但降雨带来的低温高湿将致使霜霉病菌以指数级的速度生长繁殖,病害迅速流行。所以,在条件不足时,可以把降雨应作为霜霉病防治的主要监测因素。
(3)霜霉病防治要把握三个关键时期:卵孢子萌发期、霜霉病初侵染期及病害流行后的降雨期。卵孢子萌发期,利用霜霉病卵孢子萌发模型预报霜霉病菌卵孢子的萌发期,在卵孢子萌发后,释放出来的孢子囊或游动孢子相对卵孢子较容易被杀死。4月～7月均有卵孢子萌发期,但是在4月～5月,霜霉病菌卵孢子的萌发不会致使初侵染的发生,属于无效萌发;只有在6月～7月初的卵孢子的萌发才具备足够的环境条件促使初侵染的发生,属于有效萌发,这个时间段的卵孢子萌发期才是防治的关键时期。霜霉病初侵染期,该时期可通过准确预测的卵孢子萌发期及构成侵染的潜育期推算出,并采取化学防治将可大大减缓霜霉病的发展。病害流行后的降雨期,降雨造成的高湿度和较低的温度均有利于霜霉病的复侵染,采取化学防治措施可阻止霜霉病害的流行。
(4)人工神经网络运用于葡萄霜霉病流行的预测具有很高的精确率,使用最大误差法计算其精确率,高达93.554%。所以,使用人工神经网络构建葡萄霜霉病复侵染模型是可行的,而且该模型能准确预测霜霉病的流行趋势。
(5)在霜霉病卵孢子萌发模型和霜霉病复侵染模型的研究的基础上,使用VC++程序语言构建了葡萄霜霉病预警系统。
Grape downy mildew, one of the most serious diseases, is harmful to China's grape production. It's the most important event to effectively forecast downy mildew during grape production management. Purpose of this paper was getting an effective way to predict downy mildew, and building a Grape downy mildew early warning system. In this paper, the oospores germination model of plasmopara viticola, the application of artificial neural network in forecasting grape downy mildew, building the grape downy mildew early warning system, the situation of plasmopara viticola oospores germination and the disease prevalence in the past three years were studied in Yangling district, and the results showed:
The oospores germination model was accurate in forecasting Oospores germination, and the precision was 85.966 percent. So the model can accurately predict the oospores germination period, and it can be used to guide the control of downy mildew in the course of grape production management.
Rainfall period is the most critical period in the course of the control of downy mildew. Rainfall would lead to low temperature and high humidity in summer. The low temperature and high humidity would help to Oospores germination, the first infection and distribution of downy mildew. While we lack of information concerned, rainfall should be believed as the most important fact in prediction.
If we want to control the downy mildew well, we must consider the three crucial periods: the oospores germination period, the first infection of downy mildew period and the rainfall period.
The model of forecasting grape downy mildew, based on Artificial Neural Network, is precise and effective in forecasting downy mildew. And the precision was 93.554 percent. So, it's available to predict the trend of downy mildew by Artificial Neural Network model. The trend of downy mildew will be predicted effectively.
Based on the research of the oospores germination model and the grape downy mildew forecasting model, the Grape downy mildew early warning system was built by the computer program VC++.
引文
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