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Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation

Abstract: The occurrence of crop pests and diseases always affects the development of agriculture seriously, while pest meteorology showed that climate is important in affecting the occurrence. Recently, recurrent neural network (RNN) has been broadly applied in various fields, which was designed for modeling sequential data and has been testified to be quite efficient in time series problem. This paper proposes to use bi-directional RNN with long short-term memory (LSTM) units for predicting the occurrence of cotton pests and diseases with climate factors. First, the problem of occurrence prediction of pests and diseases is formulated as time series prediction. Then the bi-directional LSTM network (Bi-LSTM) is adopted to solve the problem, which can capture long-term dependencies on the past and future contexts of sequential data. Experimental results showed that Bi-LSTM shows good performance on the occurrence prediction of pests and diseases in cotton fields, and yields an Area Under the Curve (AUC) of 0.95. This work further verified that climate indeed have strong impact on the occurrence of pests and diseases, and circulation parameters also have certain influence.