{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T06:51:58Z","timestamp":1778309518492,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"S25","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-019-3262-y","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T09:02:35Z","timestamp":1577178155000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network"],"prefix":"10.1186","volume":"20","author":[{"given":"Qingxin","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Weilu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuanzhong","family":"Kai","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"issue":"5","key":"3262_CR1","first-page":"385","volume":"19","author":"JJ Cui","year":"2007","unstructured":"Cui JJ, Chen HY, Zhao XH, Luo JY. Research course of the cotton ipm and its prospect. Cotton Sci. 2007; 19(5):385\u201390.","journal-title":"Cotton Sci"},{"issue":"6","key":"3262_CR2","first-page":"831","volume":"46","author":"KM Wu","year":"2009","unstructured":"Wu KM, Lu YH, Wang ZY. Advance in integrated pest management of crops in china. Chinese Bull Entomol. 2009; 46(6):831\u20136.","journal-title":"Chinese Bull Entomol"},{"key":"3262_CR3","unstructured":"Piatesket-Shapiro G, Piatesky-Shapiro G, Frawley WJ. Discovery, analysis, and presentation of strong rules. Menlo Park: AAAI\/MIT Press; 1991. pp. 229\u2013238."},{"key":"3262_CR4","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1007\/978-3-642-22688-5_8"},{"key":"3262_CR5","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1109\/ICCET.2009.244"},{"issue":"3","key":"3262_CR6","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.sbi.2012.03.012","volume":"22","author":"AY Sim","year":"2012","unstructured":"Sim AY, Minary P, Levitt M. Modeling nucleic acids. Curr Opin Struct Biol. 2012; 22(3):273\u20138.","journal-title":"Curr Opin Struct Biol"},{"key":"3262_CR7","first-page":"100","volume":"B09","author":"J Luo","year":"2017","unstructured":"Luo J, Shuai Z, Ren X, Limin L, Zhang L, Ji J, Yan M, Cui J. Research progress of cotton insect pests in china in recent ten years. Cotton Sci. 2017; B09:100\u201312.","journal-title":"Cotton Sci"},{"issue":"1","key":"3262_CR8","first-page":"0191116","volume":"13","author":"S Singh","year":"2018","unstructured":"Singh S, Gupta M, Pandher S, Kaur G, Rathore P, Palli SR. Selection of housekeeping genes and demonstration of rnai in cotton leafhopper, amrasca biguttula biguttula (ishida). PloS ONE. 2018; 13(1):0191116.","journal-title":"PloS ONE"},{"issue":"6","key":"3262_CR9","doi-asserted-by":"publisher","first-page":"878","DOI":"10.15252\/embr.201744205","volume":"18","author":"V Courtier-Orgogozo","year":"2017","unstructured":"Courtier-Orgogozo V, Morizot B, Bo\u00ebte C. Agricultural pest control with crispr-based gene drive: time for public debate: Should we use gene drive for pest control?Embo Rep. 2017; 18(6):878\u201380.","journal-title":"Embo Rep"},{"key":"3262_CR10","doi-asserted-by":"crossref","unstructured":"Wenzheng B, Jiang Z, Huang D-S. Novel human microbe-disease association prediction using network consistency projection. BMC Bioinformatics. 2017;18(S16). https:\/\/doi.org\/10.1186\/s12859-017-1968-2.","DOI":"10.1186\/s12859-017-1968-2"},{"issue":"15","key":"3262_CR11","doi-asserted-by":"publisher","first-page":"1855","DOI":"10.1093\/bioinformatics\/btl190","volume":"22","author":"DS Huang","year":"2006","unstructured":"Huang DS, Zeng C-H. Independent component analysis based penalized discriminant method for tumor classification using gene expression data. Bioinformatics. 2006; 22(15):1855\u201362.","journal-title":"Bioinformatics"},{"issue":"C","key":"3262_CR12","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.compag.2016.02.003","volume":"123","author":"W Ding","year":"2016","unstructured":"Ding W, Taylor G. Automatic moth detection from trap images for pest management. Comput Electron Agric. 2016; 123(C):17\u201328.","journal-title":"Comput Electron Agric"},{"issue":"1","key":"3262_CR13","first-page":"85","volume":"39","author":"WY Zhang","year":"2017","unstructured":"Zhang WY, Jing TZ, Yan SC. Studies on prediction models of dendrolimus superans occurrence area based on machine learning. J Beijing For Univ. 2017; 39(1):85\u201393.","journal-title":"J Beijing For Univ"},{"issue":"8","key":"3262_CR14","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997; 9(8):1735\u201380.","journal-title":"Neural Comput"},{"issue":"7553","key":"3262_CR15","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521(7553):436.","journal-title":"Nature"},{"key":"3262_CR16","doi-asserted-by":"crossref","unstructured":"Jurafsky JL-TL. A hierarchical neural autoencoder for paragraphs and documents. Comput Sci. 2015; v2. https:\/\/doi.org\/10.3115\/v1\/p15-1107.","DOI":"10.3115\/v1\/P15-1107"},{"key":"3262_CR17","unstructured":"Gao H, Mao J, Zhou J, Huang Z, Wang L, Xu W. Are you talking to a machine? dataset and methods for multilingual image question answering. 2015. arXiv:1505.05612."},{"key":"3262_CR18","unstructured":"Theis L, Bethge M. Generative image modeling using spatial lstms. Comput Sci. 2015. arXiv:1506.03478."},{"key":"3262_CR19","doi-asserted-by":"crossref","unstructured":"Mirshekarian S, Bunescu R, Marling C, Schwartz F. Using lstms to learn physiological models of blood glucose behavior. Conf Proc IEEE Eng Med Biol Soc. 2017. https:\/\/doi.org\/10.1109\/embc.2017.8037460.","DOI":"10.1109\/EMBC.2017.8037460"},{"key":"3262_CR20","doi-asserted-by":"crossref","unstructured":"Imielinski T, Swami A, Agrawal R. Mining association rules between sets of items in large databases. ACM SIGMOD. 1993:207\u2013216. https:\/\/doi.org\/10.1145\/170035.170072.","DOI":"10.1145\/170035.170072"},{"key":"3262_CR21","volume-title":"Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management","author":"GS Linoff","year":"1997","unstructured":"Linoff GS, Berry MJA. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Indianapolis: Wiley Publishing, Inc.; 1997."},{"key":"3262_CR22","volume-title":"Association rule based classification. Masters Theses","author":"SK Palanisamy","year":"2006","unstructured":"Palanisamy SK. Association rule based classification. Masters Theses. Worcester: Worcester Polytechnic Institute; 2006."},{"key":"3262_CR23","doi-asserted-by":"crossref","unstructured":"Miao Y, Gowayyed M, Metze F. Eesen: End-to-end speech recognition using deep rnn models and wfst-based decoding. In: Automatic Speech Recognition & Understanding: 2016. https:\/\/doi.org\/10.1109\/asru.2015.7404790.","DOI":"10.1109\/ASRU.2015.7404790"},{"key":"3262_CR24","unstructured":"Chung J, Gulcehre C, Cho KH, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. arXiv:1412.3555."},{"issue":"5","key":"3262_CR25","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Netw. 2005; 18(5):602\u201310.","journal-title":"Neural Netw"},{"key":"3262_CR26","unstructured":"Kalchbrenner N, Danihelka I, Graves A. Grid long short-term memory. 2015. arXiv:1507.01526."},{"key":"3262_CR27","unstructured":"Ruder S. An overview of gradient descent optimization algorithms. 2017. arXiv:1609.04747."},{"issue":"3","key":"3262_CR28","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/s10994-014-5456-x","volume":"99","author":"Q Qi","year":"2015","unstructured":"Qi Q, Rong J, Yi J, Zhang L, Zhu S. Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (sgd). Mach Learn. 2015; 99(3):353\u201372.","journal-title":"Mach Learn"},{"key":"3262_CR29","unstructured":"Association JS. Accuracy (trueness and precision) of measurement methods and results \u2013 part 1: General principles and definitions. Int Org Stand. 1994; ISO 5725-1-1994."},{"key":"3262_CR30","doi-asserted-by":"crossref","unstructured":"Hanley JA, Mcneil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983. https:\/\/doi.org\/10.1148\/radiology.148.3.6878708.","DOI":"10.1148\/radiology.148.3.6878708"},{"key":"3262_CR31","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Zheng X. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. 2016. arXiv:1603.04467."},{"issue":"10","key":"3262_CR32","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2013","unstructured":"Pedregosa F, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J. Scikit-learn: Machine learning in python. J Mach Learn Res. 2013; 12(10):2825\u201330.","journal-title":"J Mach Learn Res"},{"issue":"1","key":"3262_CR33","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1186\/s12864-015-1970-x","volume":"16","author":"SC Chen","year":"2015","unstructured":"Chen SC, Tsai TH, Chung CH, Li WH. Dynamic association rules for gene expression data analysis. BMC Genomics. 2015; 16(1):786.","journal-title":"BMC Genomics"},{"issue":"JUN","key":"3262_CR34","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/S0003-2670(01)85298-3","volume":"138","author":"D Coomans","year":"1982","unstructured":"Coomans D, Massart DL. Alternative k -nearest neighbour rules in supervised pattern recognition : Part 3. condensed nearest neighbour rules. Anal Chim Acta. 1982; 138(JUN):153\u201365.","journal-title":"Anal Chim Acta"},{"issue":"3","key":"3262_CR35","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995; 20(3):273\u201397.","journal-title":"Mach Learn"},{"issue":"8","key":"3262_CR36","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998; 20(8):832\u201344.","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3262-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-3262-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3262-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T00:11:16Z","timestamp":1608682276000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-3262-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":36,"journal-issue":{"issue":"S25","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3262"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-3262-y","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"24 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"688"}}