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作物学报 ›› 2009, Vol. 35 ›› Issue (2): 341-347.doi: 10.3724/SP.J.1006.2009.00341

• 耕作栽培·生理生化 • 上一篇    下一篇

应用神经网络和统计模型预测大豆生长发育阶段

张久权1,2;张凌霄2;张明华3;WATSON Clarence 4   

  1. 1中国农业科学院烟草研究所，青岛266101；2Delta Research and Extension Center, Mississippi State University,Stoneville 38776, MS,USA;3Department of Land, Air and Water Resources,University of California, Davis 95616,CA,USA;4 Oklahoma State University, Stillwater 74078-6009,OK,USA
  • 收稿日期:2008-01-07 修回日期:2008-09-10 出版日期:2009-02-12 网络出版日期:2008-12-12
  • 基金资助:

    本研究由密西西比大豆促进委员会研究基金（20021013）资助

Nrediction of Soybean Growth and Development Stages Using Artificial Neural Network and Statistical Models

ZHANG Jiu-Quan1,2,ZHANG Ling-Xiao2,ZHANG Ming-Hua3, WATSON Clarence4   

  1. 1Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao 266101,China；2Delta Research and Extension Center, Mississippi State University,Stoneville 38776, MS,USA;3Department of Land, Air and Water Resources,University of California, Davis 95616,CA,USA;4 Oklahoma State University, Stillwater 74078-6009,OK,USA
  • Received:2008-01-07 Revised:2008-09-10 Published:2009-02-12 Published online:2008-12-12

PDF

1486

被引次数

8

摘要：

预测大豆的物候期对指导大豆生产、安排农事活动等具有重大意义。本研究构建了一种简单、但有效的预测模型，使大豆种植者能够较准确地预测大豆各生育阶段的具体日期。试验地位于美国密西西比Delta研究推广中心(经度: 90°55'W，纬度: 33°25'N)。试验进行了5年，以前4年(1998—2001)数据构建模型，第5年(2002)的数据验证模型。为简化模型，杂草、病虫害、干旱等干扰因素被优化排除。采用逐步回归(SR)、神经网络(ANN)以及内插法构建模型。营养(V-stage)和生殖(R-stage)生长阶段分别建模。结果表明，通过播期(PD)和从播种到某阶段的相对平均天数能很准确地预测营养生长各阶段的具体日期；可通过播种日期和成熟期组数值(MG)准确预测生殖生长各阶段的具体日期。3种方法中，神经网络所构建的模型准确度最高，具有较好的推广应用价值。

关键词: 大豆, 生长期, 成熟期组, 神经网络, 模拟, 播种日期, 物候学

Abstract:

The prediction of soybean phenology is important in many aspects of soybean production. The study objective was to develop predictive models, using a simple and effective modeling technique, which can allow producers to predict soybean growth and development stages in their fields. The experiments were conducted at the Delta Research and Extension Center in Stoneville, Mississippi (latitude: 33°25' N, longitude: 90°55' W) under irrigated conditions. The models were constructed using four-year field data (1998 to 2001), and validated with the fifth-year data (2002). Potential factors affecting stages of soybean growth and development were considered for developing the models. Affecting factors, such as weeds, insects, diseases, and drought stress, were controlled optimally to simplify the modeling procedures. In addition, stepwise regression (SR) analysis, artificial neural networks (ANN), and interpolation approaches were used to construct the models. The modeling of soybean growth and development processes was separated into two distinct periods: vegetative growth stage (V-stage) and reproductive growth stage (R-stage). The models included ten V-stages (up to V8) and eight R-stages. In the V-stage models, PD (planting date) and mean relative time-span from planting to a particular stage were the only significant parameters, whereas in R-stage models, PD and MG (maturity group) were significant. The models obtained accurate predictions when only using PD, MG, and mean relative time-span from planting to a particular stage. The ANN method provided the greatest accuracy in predicting phenological events, indicating that the ANN method can be effectively applied in crop modeling.

Key words: Soybean, Growth stage, Maturity group, Artificial neural networks, Modeling, Planting date, Phenology

[1]Bernard R L. Two major genes for time of flowering and maturity in soybeans. Crop Sci, 1971, 11: 242–244
[2]Buzzel R I, Voldeng H D. Inheritance of insensitivity to long day length. Soybean Genet News, 1980, 7: 26–29
[3]McBlain B A, Bernard R L. A new gene affecting the time of flowering in soybeans. J Hered, 1987, 78: 160–162
[4]Borthwick H A, Parker M W. Effective of photoperiodic treat-ments of plants of different age. Bot Gaz, 1938, 100: 245–249
[5]Cregan P B, Hartwig E E. Characterization of flowering response to photoperiod in diverse soybean genotypes. Crop Sci, 1984, 24: 657–660
[6]Johnson H W, Brothwick H A, Leffel R C, Effects of photoperiod and time of planting on rate of development of soybeans in vari-ous stages of life cycle. Bot Gaz, 1960, 122: 77–95
[7]Thomas F J, Raper C D Jr. Photoperiodic control of seed filling for soybeans. Crop Sci, 1976, 16: 667–672
[8]Cartter J L, Hartwig E E. The Management for Soybeans. In: Norman A G ed. The Soybean. New York: Academic Press, 1963, pp 161–226
[9]Acock B, Pachepsky Y A, Acock M C, Reddy V R, Whisler F D. Modeling soybean cultivars development rate, using field data from the Mississippi valley. Agron J, 1997, 89: 994–1002
[10]Foroun N, Mundel H H, Saindon G, Entz T. Effect of level and timing of moisture stress on soybean plant development and yield components. Irrig Sci, 1993, 13: 149–155
[11]Jones P G, Laing D R. Simulation of the phenology of soybeans. Agric Syst, 1978, 3: 295–311
[12]Specht J E, Elmore R W, Eisenhauer D E, Klocke N W. Growth stage scheduling criteria for sprinkler-irrigated soybeans. Irrig Sci, 1991, 10: 99–111
[13]Welch S M, Jones J W, Brennan M W, Reeder G, Jacobson B M. PC Yield: Model-based decision support for soybean production. Agric Syst, 2002, 74: 79–98
[14]Blackard J A, Dean D J. Comparative accuracies of artificial neural networks and discriminate analysis in predicting forest cover types from cartographic variables. Comput Electron Agric, 1999, 24: 131–151
[15]Elizondo D R, McClendon R W, Hoogenboom G. Neural network models for predicting flowering and physiological maturity of soybean. Transact ASAE, 1994, 37: 981–988
[16]Zhang L X, Zhang J, Kyei-Boahen S, Watson C E. Developing phenological prediction tables for soybean. Crop Management. doi: 10.1094/ CM-2004-1025-01-RS (2004-10-25)
[2008-01-04] http://www.plantmanagementnetwork.org/pub/cm/research/2004/tables/.
[17]Fehr W R, Caviness C E. Stage of soybean development. In: Iowa State University Cooperative Extension Service Special Report, 1977, p 80
[18]Bishop C M. Neural Networks for Pattern Recognition. Oxford, England: Clarendon Publishing, 1995
[19]Jain A, Mao J. Artificial Neural Networks: A tutorial. Computer, 1996, 29: 31–44
[20]Mitchell T M. Machine Learning. Boston, MA: McGraw-Hill Co, 1997
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