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.
CITATION STYLE
Xiao, Q., Li, W., Chen, P., & Wang, B. (2018). Prediction of Crop Pests and Diseases in Cotton by Long Short Term Memory Network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10955 LNCS, pp. 11–16). Springer Verlag. https://doi.org/10.1007/978-3-319-95933-7_2
Mendeley helps you to discover research relevant for your work.