基于大数据的玉米田四代棉铃虫发生量的预测模型

doi:10.11959/j.issn.2096-0271.2016008

Abstract

Abstract:

The monitoring and forecasting model was put forward based on support vector machine program.According to the data collection of the fourth generation occurrence degree of the corn bollworm in Shandong province from 1999 to 2013,the support vector regression (SVR) method was adopted to build the nonlinear correlation model between the occurrence degree of the fourth generation bollworm and the associated factors.The method and the model were tested and analyzed.The results showed that the SVR forecasting model for prediction was almost in accord with the actual insect occurrence situation.The mean absolute percentage error was 4.36%,and the actual and estimated value of the correlation coefficient was 0.960 6.It could provide effective and accurate guidance to the cotton bollworm control in corn fields.

Key words: agricultural big data, cotton bollworm, support vector regression, monitoring and forecasting, corn

CLC Number:

S431.9

Lei ZHAO, Bo YANG, Yong LIU, Shaomin MU, Fujiang WEN. Forecasting model for the fourth generation of cotton bollworm in corn fields based on big data[J]. Big Data Research, 2016, 2(1): 68-75.

Figures/Tables 6

References 19

[1]	GANDOMI A , HAIDER M . Beyond the hype:big data concepts,methods,and analytics[J]. International Journal of Information Management, 2015,35(2):137-144.
[2]	KAMBATLA K , KOLLIAS G , KUMAR V , et al. Trends in big data analytics[J]. Journal of Parallel and Distributed Computing, 2014,74(7): 2561-2573.
[3]	LAZER D , KENNEDY R , KING G , et al. The parable of Google flu:traps in big data analysis[J]. Science, 2014,343(6176): 1203-1205.
[4]	SUN Z F , DU K M , ZHENG F X . Perspectives of research and application of big data on smart agriculture[J]. Journal of Agricultural Science and Technology, 2013,15(6): 63-71.
[5]	SIRAJ A S , SANTOS-VEGA M , BOUMA M J , et al. Altitudinal changes in malaria incidence in highlands of ethiopia and colombia[J]. Science, 2014,343(6175): 1154-1158.
[6]	HALLMANN C A , FOPPEN R P B , TURNHOUT C A M V , et al. Declines in insectivorous birds are associated with high neonicotinoid concentrations[J]. Nature, 2014,511(7509): 341-343.
[7]	宋长青, 牟少敏, 周虎 , 等. 农业大数据研究中心的建设、研究与实践[J]. 中国现代教育装备, 2015(3):8-11.
	SONG C Q , MU S M , ZHOU H , et al. Construction,research and practice of agricultural big data research center[J]. China Modern Educational Equipment, 2015(3):8-11.
[8]	辛妍, . 大数据在农业中的应用[J]. 新经济导刊 2015(4):46-51.
	XIN Y . Big data applications in agriculture[J]. New Economy Weekly, 2015(4):46-51.
[9]	温孚江, . 农业大数据与发展新机遇[J]. 中国农村科技 2013(3):4-7.
	WEN F J . Agricultural big data and development of new opportunities[J]. Agricultural Sciences in China, 2013(3):4-7.
[10]	杨波, 刘勇, 牟少敏 , 等. 大数据背景下山东省二代玉米螟发生程度预测模型的构建[J]. 计算机研究与发展, 2014(s2):160-165.
	YANG B , LIU Y , MU S M , et al. Based on big data:the establishment of meteorological forecast model for the occurrence degree of the second generation of corn borer in Shandong[J]. Journal of Computer Research and Development, 2014(s2):160-165.
[11]	陈广泉, . 河西走廊玉米田棉铃虫发生规律与药剂防治技术研究[D]. 咸阳：西北农林科技大学 2004:12-16.
	CHEN G Q . The occurrence regularity and cottrol technology research of cotton bollworm in the hexi corridor cornfield[D]. Xianyang:Northwest A＆amp;F University, 2004:12-16.
[12]	CHANGNON D , SANDSTROM M , ASTOLFI J , et al. Using climatology to predict the first majorsummer corn earworm (lepidoptera:noctuidae) catch in north central Illinois[J]. Meteorological Applications, 2010,17(3): 321-328.
[13]	FENG H , GOULD F , HUANG Y , et al. Modeling the population dynamics of cotton bollworm Helicoverpa armigera (Hübner) (lepidoptera:noctuidae) over a wide area in northern China[J]. Ecological Modelling, 2010,221(15): 1819-1830.
[14]	RAJ K R , KARDAM A , ARORA J K , et al. Artificial neural network (ANN) design for Hg–Se interactions and their effect on reduction of Hg uptake by radish plant[J]. Journal of Radioanalytical and Nuclear Chemistry, 2010,283(3): 797-801.
[15]	VAPNIK V N . The nature of statistical learning theory[J]. IEEE Transactions on Neural Networks, 1995,10(5):988-999.
[16]	赵仲华, 沈佐锐 . 昆虫种群动态非线性建模理论与应用[J]. 生物数学学报, 2001,16(4):439-447.
	ZHAO Z H , SHEN Z R . Insect population dynamics of nonlinear modeling theory and application[J]. Journal of Biomathematics, 2001,16(4):439-447.
[17]	李永娜, . 基于支持向量机的回归预测综述[D]. 信息通信 2014(11):32-33.
	LI Y N . Regression forecast review based on support vector machine[D]. Message Communication, 2014(11):32-33.
[18]	ERDAL H I . Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction[J]. Engineering Applications of Artificial Intelligence, 2013,26(7):1689-1697.
[19]	朱军生, 翟保平, 董保信 . 基于径向基小波网络的二代棉铃虫卵峰日预测模型[J]. 昆虫学报, 2010,53(12):1429-1435.
	ZHU J S , ZHAI B P , DONG B X . Forecasting model for the oviposition peak day in the second generation of helicoverpa armigera (lepidopeter:noctuidae) based on radial basis wavelet network[J]. Acta Entomologica Sinica, 2010,53(12):1429-1435.

Metrics

Recommended 0

No Suggested Reading articles found!

年份	真实值	拟合值	绝对误差	相对误差
1999年	15.5	15.599 8	- 0.099 8	0.64%
2000年	49	48.899 8	0.100 2	0.20%
2001年	14	14.099 8	- 0.099 8	0.71%
2002年	24.5	24.399 9	0.100 1	0.41%
2003年	5	5.100 1	- 0.100 1	2.00%
2004年	19	18.574 9	0.425 1	2.24%
2005年	16	16.100 3	- 0.100 3	0.63%
2006年	39.3	37.699 3	1.600 7	4.07%
2007年	42.5	42.399 8	0.100 2	0.24%
2008年	47	47.100 3	0.100 3	0.21%
2009年	34.5	34.600 3	- 0.100 3	0.29%
2010年	38	38.099 9	- 0.1	0.26%

年份	真实值	预测值	绝对误差	相对误差
2011年	52	52.992 3	- 0.992 3	1.90%
2012年	42.6	41.243 2	1.356 8	3.17%
2013年	32.5	35.101 7	- 2.601 8	8.01%

	MAE	MRE	RMSE
训练值	0.24	0.99%	1.68
预测值	1.65	4.36%	3.1

Forecasting model for the fourth generation of cotton bollworm in corn fields based on big data

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 19

Related Articles 8

Metrics

Recommended 0

[1]	Lei CUI. Demand and model of agricultural big data construction in China and the way to promote the whole industry chain [J]. Big Data Research, 2019, 5(5): 100-108.
[2]	Zhongfu SUN, Jinxiang CHU, Juncheng MA, Keming DU, Feixiang ZHENG. Preliminary analysis and exploration of big data service for agriculture,countryside and farmers [J]. Big Data Research, 2017, 3(3): 33-43.
[3]	Wensheng WANG, ONGChangqing GUO. Envisagement of the construction of national agricultural big data center [J]. Big Data Research, 2016, 2(1): 28-34.
[4]	Junqing LI, Changqing SONG, Hu ZHOU. Challenge and thinking of agricultural big data assets management [J]. Big Data Research, 2016, 2(1): 35-43.
[5]	Yong ZHENG, Lei MENG, Wenjing LI. Study on the agricultural big data development of Shandong province [J]. Big Data Research, 2016, 2(1): 44-52.
[6]	Shaomin MU, Fujiang WEN, Changqing SONG. Training mode of graduate students majored in agricultural big data [J]. Big Data Research, 2016, 2(1): 53-58.
[7]	Qingqing ZHANG, Yong LIU, Shaomin MU, Fujiang WEN. Decision tree predictive classification model on the occurrence degree of wheat aphids based on big data [J]. Big Data Research, 2016, 2(1): 59-67.
[8]	Chende WANG, Nan WANG, Danxing CAO. Opportunities and challenges of commodity exchange bring form agricultural big data [J]. Big Data Research, 2016, 2(1): 76-85.