Author:
Xiao Qingxin,Li Weilu,Kai Yuanzhong,Chen Peng,Zhang Jun,Wang Bing
Abstract
Abstract
Background
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. In recent years, machine learning and especially deep learning methods have been widely used in many fields and have achieved good results.
Methods
First, 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.
Results
The 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.
Conclusion
Suitable 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.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Reference36 articles.
1. Cui JJ, Chen HY, Zhao XH, Luo JY. Research course of the cotton ipm and its prospect. Cotton Sci. 2007; 19(5):385–90.
2. Wu KM, Lu YH, Wang ZY. Advance in integrated pest management of crops in china. Chinese Bull Entomol. 2009; 46(6):831–6.
3. Piatesket-Shapiro G, Piatesky-Shapiro G, Frawley WJ. Discovery, analysis, and presentation of strong rules. Menlo Park: AAAI/MIT Press; 1991. pp. 229–238.
4. Galitsky BA, Dobrocsi G, Rosa JLDL, Kuznetsov SO. Using generalization of syntactic parse trees for taxonomy capture on the web. In: International Conference on Conceptual Structures for Discovering Knowledge: 2011. https://doi.org/10.1007/978-3-642-22688-5_8.
5. Hu Z. Design of intrusion detection system based on a new pattern matching algorithm. In: International Conference on Computer Engineering & Technology: 2009. https://doi.org/10.1109/iccet.2009.244.
Cited by
39 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献