Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network

Qingxin Xiao,Jun Zhang,Weilu Li,Bing Wang,Yuanzhong Kai,Peng Chen

doi:10.1186/s12859-019-3262-y

Qingxin Xiao, Jun Zhang + Show 4 more

Open Access

https://doi.org/10.1186/s12859-019-3262-y

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Abstract

BackgroundThe occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, machine learning and especially deep learning methods have been widely used in many fields and have achieved good results.MethodsFirst, this papaer used the common Aprioro algorithm to find the association rules between weather factors and the occurrence of cotton pests. Then, in this paper, the problem of predicting the occurrence of pests and diseases is formulated as time series prediction, and an LSTM-based method was developed to solve the problem.ResultsThe association analysis reveals that moderate temperature, humid air, low wind spreed and rain fall in autumn and winter are more likely to occur cotton pests and diseases. The discovery was then used to predict the occurrence of pests and diseases. Experimental results showed that LSTM performs well on the prediction of occurrence of pests and diseases in cotton fields, and yields the Area Under the Curve (AUC) of 0.97.ConclusionSuitable temperature, humidity, low rainfall, low wind speed, suitable sunshine time and low evaporation are more likely to cause cotton pests and diseases. Based on these associations as well as historical weather and pest records, LSTM network is a good predictor for future pest and disease occurrences. Moreover, compared to the traditional machine learning models (i.e., SVM and Random Forest), the LSTM network performs the best.

Highlights

Cotton is an important economic crop, which occupies a important position in the national economy
Several time series of weather features are applied in the occurrence of pests, including Maximum Temperature Maximum temperature (MaxT) ◦C, Minimum Temperature Minimum temperature (MinT) ◦C, Relative Humidity in the morning (RH1 (%)), Relative Humidity in the evening (RH2 (%)), Rainfall (RF), Wind Speed (WS), Sunshine Hour (SSH) and Evaporation (EVP)
All the results indicate that the Long short term memory (LSTM) network is suitable for the prediction of cotton pests and diseases, which lays a theoretical foundation for practical application in the future

Summary

Introduction

Cotton is an important economic crop, which occupies a important position in the national economy. Cotton was always damaged by various pests and diseases during its growth. Association rule analysis is one of the important methods in data mining, which is a rule-based machine learning method for discovering interesting relations between variables in large databases. Association rule mining is applied in many fields including webpage mining [4], intrusion detection [5], continuous production, and bioinformatics [6]. This paper attempted to further verify the correlation between weather factors and pest occurrence through correlation rule analysis, and to explore the potential laws of pest occurrence and weather changes. The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. Machine learning and especially deep learning methods have been widely used in many fields and have achieved good results

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