基于改进深度置信网络的大棚冬枣病虫害预测模型

导致冬枣病虫害发生的原因很多而且很复杂,利用传统的数学方法和神经网络(neural network,NN)很难建立正确的病虫害预测模型.由于典型的深度置信网络(deep belief network,DBN)的各层之间缺乏有监督训练,使得网络误差逐层向上传递,降低了预测模型的预测率.针对这些问题,引入冬枣病虫害的先验信息,提出一种基于环境信息和改进DBN的冬枣病虫害预测模型.在该模型中,通过无监督训练和有监督微调从冬枣生长的环境信息序列中获取可表征冬枣病虫害发生的深层特征的隐层参数,并形成新的特征集,然后在预测模型的顶层通过一个后向传播神经网络(back propagation neural network,BPNN)进行病虫害预测.从2014—2017年的4 a时间内,利用农业物联网传感器采集30个大棚冬枣常见的2种虫害和3种病害发生的环境信息序列6000多条,由此验证所提出的预测模型,平均预测正确率高达84.05%.与基于强模糊支持向量机、改进型NN和BPNN的3种病虫害预测模型进行了试验比较,预测正确率提高了20多个百分点.试验结果表明,该模型极大提高了大棚冬枣病虫害的预测正确率.该研究可为大棚冬枣病虫害预测提供技术参考.

Disease and insect pest forecasting model of greenhouse winter jujube based on modified deep belief network

Abstract: The diseases and insect pests of greenhouse winter jujube are one of the main factors that restrict the yield and quality of winter jujube. The timely prediction of the jujube diseases and insect pests is the prerequisite to prevent and control diseases and insect pests. It is difficult to establish an accurate forecasting model of diseases and insect pests using traditional mathematical method and neural network (NN) because of many complex factors that lead to the occurrence of diseases and insect pests of winter jujube, including the meteorological conditions (such as temperature, sunlight, humidity), soil conditions (such as moisture, soil heavy metals), and biological characteristics (such as roots, leaves). During the process of forecasting model training, due to the defects of artificial design features and the unpredictable complexity in the design process, the accuracy of disease and insect pest prediction and the efficiency of the design features can''t have a lot of space of ascension. It is possible to automatically forecast diseases and insect pests of winter jujube with the development of agricultural IOT (Internet of Things), smart camera equipment, high performance and large capacity data storage, computer and network technology as well as the massive complex data processing technology. Faced with the problem of complexity and uncertainty of diseases and insect pests prediction of winter jujube, a forecasting model of winter jujube diseases and insect pests is proposed based on the modified deep belief network (DBN). Due to the merits of the DBN, the prediction model of disease and insect pest based on modified DBN can not only utilize 20 kinds of environmental information data, but also introduce the similarity between the prior information and the constraints of the current information. The modified DBN consists of a visible input layer, several hidden layers, and an output layer. The visible layer inputs the data, whose range has been normalized into 0,1]; the hidden layers are invisible, in which binary values are used, and activated by the sigmoid kernel function. Via simulating neural connecting structure of human brain and introducing the supervised information by restricting the similarity between feature vectors in the learning process, the proposed model can automatically learn senior nonlinear hierarchical combination features from the environmental information of winter jujube growth, which is suitable for data classification and importing high-level features into traditional BP (back propagation) neural network classifier to improve the disease forecasting precision. The disease and insect pest prediction is conducted by BP network in the top level of DBN. Experiments on the actual database of disease and insect pest of greenhouse winter jujube are performed. After a large number of training samples and training times, the prediction accuracy rate of diseases and insect pests is greatly improved. The accuracy rate of forecasting result is over than 84%. The experimental results show that the proposed model has provided a technical basis and support for the automatic crop disease forecasting with environmental information obtained in fields, and has great application prospect in disease and insect pest prediction of greenhouse winter jujube. As there are many factors affecting crop diseases, practically, some factors vary with the time, how to use the environmental information of crop growth to build a powerful and practical crop disease forecasting method still needs further study.