本文選題：病害 + 預測　； 參考：《農(nóng)業(yè)工程學報》2017年19期

【摘要】：導致冬棗病蟲害發(fā)生的原因很多而且很復雜,利用傳統(tǒng)的數(shù)學方法和神經(jīng)網(wǎng)絡(neural network,NN)很難建立正確的病蟲害預測模型。由于典型的深度置信網(wǎng)絡(deep belief network,DBN)的各層之間缺乏有監(jiān)督訓練,使得網(wǎng)絡誤差逐層向上傳遞,降低了預測模型的預測率。針對這些問題,引入冬棗病蟲害的先驗信息,提出一種基于環(huán)境信息和改進DBN的冬棗病蟲害預測模型。在該模型中,通過無監(jiān)督訓練和有監(jiān)督微調(diào)從冬棗生長的環(huán)境信息序列中獲取可表征冬棗病蟲害發(fā)生的深層特征的隱層參數(shù),并形成新的特征集,然后在預測模型的頂層通過一個后向傳播神經(jīng)網(wǎng)絡(back propagation neural network,BPNN)進行病蟲害預測。從2014—2017年的4 a時間內(nèi),利用農(nóng)業(yè)物聯(lián)網(wǎng)傳感器采集30個大棚冬棗常見的2種蟲害和3種病害發(fā)生的環(huán)境信息序列6 000多條,由此驗證所提出的預測模型,平均預測正確率高達84.05%。與基于強模糊支持向量機、改進型NN和BPNN的3種病蟲害預測模型進行了試驗比較,預測正確率提高了20多個百分點。試驗結(jié)果表明,該模型極大提高了大棚冬棗病蟲害的預測正確率。該研究可為大棚冬棗病蟲害預測提供技術(shù)參考。
[Abstract]:There are many and complicated reasons for the occurrence of diseases and insect pests in winter jujube. It is difficult to establish a correct prediction model of diseases and pests by using traditional mathematical methods and neural network (NN). Due to the lack of supervised training among the layers of the typical deep belief network, the network errors are transmitted upward, and the prediction rate of the prediction model is reduced. In order to solve these problems, a prediction model of winter jujube disease and insect pests based on environmental information and improved DBN was proposed by introducing the prior information of winter jujube pests and diseases. In this model, the hidden layer parameters can be obtained from the environmental information sequence of Dongzao jujube growth by unsupervised training and supervised fine-tuning, and a new feature set is formed. The disease and insect pests are predicted at the top of the prediction model through a back propagation neural network (BPNN). In the period of 2014-2017, more than 6 000 environmental information sequences of two common pests and three diseases of winter jujube in greenhouse were collected by using agricultural Internet of things sensors, and the proposed prediction model was verified. The average prediction accuracy was 84.05%. Compared with three forecasting models based on strong fuzzy support vector machine, improved NN and BPNN, the accuracy of prediction is increased by more than 20 percentage points. The experimental results show that the prediction accuracy of winter jujube diseases and insect pests in greenhouse is greatly improved by this model. This study can provide technical reference for prediction of diseases and pests of winter jujube in greenhouse.
【作者單位】： 西京學院信息工程學院;
【基金】：國家自然科學基金項目(61473237) 陜西省自然科學基礎(chǔ)研究計劃(2016GY-141)
【分類號】：S436.65;TP18

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