Prediction of chilling damage risk in maize growth period based on probabilistic neural network approach
Keywords:
maize chilling damage, risk prediction, probabilistic neural networkAbstract
Low temperature chilling damage is one of the most serious disasters in maize production, which is a typical non-linear complex issue with numerous influencing factors and strong uncertainty. How to predict it is not only a hot theoretical research topic, but also an urgent practical problem to be solved. However, most of the current researches are focusing on post-disaster static descriptive assessment rather than pre-disaster dynamic predictive analysis, resulting in the problems such as no indicative result and low accuracy. In this study, the satisfaction rate of environmental accumulated temperature for maize production was used to measure the chilling damage risk, and a model for maize chilling damage risk prediction based on probabilistic neural network was constructed. The model was composed of input layer, pattern layer, summation layer and output layer. The obtained results showed that the prediction accuracy for the most serious risk level was as high as 0.91, and the rates of the Type I Error and Type II Error made by the model were 0.1 and 0.09, respectively. This indicated that the model employed was promising with good performance. The results of this research is are of both theoretical significance for providing a new reference method of pre-disaster prediction to study maize chilling disaster risk and practical significance for reducing maize production risk and ensuring yield safety. Keywords: maize chilling damage, risk prediction, probabilistic neural network DOI: 10.25165/j.ijabe.20211402.5732 Citation: Mi C Q, Zhao C H, Deng Q Y, Deng X W. Prediction of chilling damage risk in maize growth period based on probabilistic neural network approach. Int J Agric & Biol Eng, 2021; 14(2): 120–125.References
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[2] Ma S Q, Liu Y Y, Wang Q. Dynamic prediction and evaluation method of maize chilling damage. Chinese Journal of Applied Ecology, 2006; 17(10): 1905–1910. (in Chinese)
[3] Zhu H R, Liu H N, Zhang H L, Yu H M. Evaluation and projection of maize cold damage in Heilongjiang. Climate Change Research, 2015; 11(3): 173–178. (in Chinese)
[4] Chen K Q, Mi N. Risk Evaluation on maize cold damage and frost damage in Liaoning province. Journal of Meteorology and Environment, 2016; 32(1): 89–94. (in Chinese)
[5] Wang C Y, Cai J J, Zhang J Q. Risk assessment of drought and chilling
injury of maize in Northeast China. Transactions of the CSAE, 2015; 31(6): 238–245. (in Chinese)
[6] Wang C Y, Zhang J Q, Huo Z G, Cai J J, Liu X P, Zhang Q. Prospects and progresses in the research of risk assessment of agro-meteorological disasters. Acta Meteorologica Sinica, 2015; 73(1): 1–19. (in Chinese)
[7] Gao Y F, Wang M, Ma C, Jia W D, Lu Z G, Long C. Single and multi-factor analysis on screw-holding power of corn straw fiber brick. Construction and Building Materials, 2018; 181: 579–587.
[8] Ma S Q, Wang Q, Wang C Y, Huo Z G. The risk division on climate and economic loss of maize chilling damage in Northeast China. Geographical Research, 2008; 27(5): 1169–1177. (in Chinese)
[9] Gao X R, Wang C Y, Zhang J Q. Spatial-temporal distribution and multiple-temporal scale variation laws of chilling damage of maize in Northeast China. Journal of Catastrophology, 2012; 27(4): 65–70. (in Chinese)
[10] Gao X R, Wang C Y, Zhang J Q. The impacts of global climatic change on chilling damage distributions of maize in Northeast China. Acta Ecologica Sinica, 2012; 32(7): 2110–2118. (in Chinese)
[11] Zhu H X, Lyu J J, Yan P, Qu H H, Wang P, Yu Y N, et al. Identification on cold damage year based on accumulated equivalent temperature during rice growth season in cold region. Chinese Journal of Agrometeorology, 2019; 40(6): 380–390. (in Chinese)
[12] Ma S Q, Li X F, Jin L F, Xi Z X, Deng K C, Liu X H. Effects of cold accumulated temperature in different sub-flowering period on Japonica rice seed setting and chilling injury indexes in Northeast China. Journal of Natural Disasters, 2019; 28(2): 153–159. (in Chinese)
[13] Li W L, Zhang D Y, Zhang L J. Risk assessment and zoning of meteorological disaster in Heilongjiang province. Arid Land Geography, 2009; 32(5): 754–760. (in Chinese)
[14] Lin J, Chen J J, Wang J Y, Li L C, Ma Z G, Yang K, et al. Frost disaster risk assessment of crop in Fujian province based on information diffusion theory. Chinese Journal of Agrometeorology, 2011; 32(Sl): 188–191. (in Chinese)
[15] Cai D X, Zhang J H, Liu S J. Analysis and division for cold damage to litchi yield in Hainan. Chinese Journal of Agrometeorology, 2013; 34(5): 595–601. (in Chinese)
[16] Lin X M, Yue Y J, Su Y. Frost hazard risk assessment of winter wheat: based on the meteorological indicator at different growing stages. Journal of Catastrophology, 2009; 24(4): 45–50. (in Chinese)
[17] Ma S Q, Xi Z X, Wang Q. Risk evaluation of cold damage to corn in Northeast China. Journal of Natural Disasters, 2003; 12(3): 137–141. (in Chinese)
[18] Cai J J, Wang C Y, Zhang J Q. Hazard assessment of drought disaster and chilling damage of various growth stages of maize in Northeast China. Acta Meteorologica Sinica, 2013; 71(5): 976–986. (in Chinese)
[19] Wang C Y, Zhang X F, Zhao Y X. Impact evaluation and risk assessment of agrometeorological disasters. Beijing: China Meteorological Press, 2010; pp.262–282. (in Chinese)
[20] Zhang X F, Yu W D, Wang C Y. Risk evaluation for spring frost disaster of winter wheat in Yellow River-Huai River regions based on crop model. Plateau Meteorology, 2012; 31(1): 277–284. (in Chinese)
[21] Zhang H Y. Research on crop pest forecasting based on adaptive probabilistic neural network. Master dissertation. Lanzhou: Lanzhou Jiaotong University, 2011; 51p.
[22] Behshad M, Amirhessam T, Anke M B, Amir H G. Probabilistic neural networks: a brief overview of theory, implementation, and application. In: Handbook of Probabilistic Models. Butterworth-Heinemann, 2020; pp. 347–367.
[23] Jiang Q S, Shen Y H, Li H, Xu F Y. New fault recognition method for rotary machinery based on information entropy and a probabilistic neural network. Sensors, 2018; 18(2): 337–349.
[24] Shilaja C, Arunprasath T. Energy demand classification by probabilistic neural network for medical diagnosis applications. Neural Computing and Applications, 2020; 32: 11129–11136.
[25] Alweshah M, Ramadan E, Ryalat M, Almi’ani M, Hammouri A I. Water evaporation algorithm with probabilistic neural network for solving classification problems. Jordanian Journal of Computers and Information Technology, 2020; 6(1): 1–15.
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Published
2021-04-03
How to Cite
Mi, C., Zhao, C., Deng, Q., & Deng, X. (2021). Prediction of chilling damage risk in maize growth period based on probabilistic neural network approach. International Journal of Agricultural and Biological Engineering, 14(2), 120–125. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5732
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Natural Resources and Environmental Systems
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