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Prediction of Crop Pests and Diseases in Cotton by Long Short Term Memory Network

  • Qingxin Xiao
  • Weilu Li
  • Peng Chen
  • Bing Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

This paper aims to predict the occurrence of pests and diseases for cotton based on long short term memory (LSTM) network. First, the problem of occurrence of pests and diseases was formulated as time series prediction. Then LSTM was adopted to solve the problem. LSTM is a special kind of recurrent neutral network (RNN), which introduces gate mechanism to prevent the vanished or exploding gradient problem. It has been shown good performance in solving time series problem and can handle the long-term dependency problem, as mentioned in many literatures. The experimental results showed that LSTM performed good on the prediction of occurrence of pests and diseases in cotton fields, and yielded an Area Under the Curve (AUC) of 0.97. The paper further verified that the weather factors indeed have strong impact on the occurrence of pests and diseases, and the LSTM network has great advantage on solving the long-term dependency problem.

Keywords

Long short term memory Weather factors Recurrent neural network Occurrence of pests and diseases 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Nos. 61672035, 61300058 and 61472282).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Physical Science and Information TechnologyAnhui UniversityHefeiChina
  2. 2.School of Electrical and Information EngineeringAnhui University of TechnologyMa’anshanChina

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