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Volumn 12, Issue 2, 2014, Pages 313-328

Predictive ability of machine learning methods for massive crop yield prediction

Author keywords

K nearest neighbor; Multiple linear regression; Neural networks; Regression trees; Support vector regression

Indexed keywords


EID: 84901831679     PISSN: 1695971X     EISSN: None     Source Type: Journal    
DOI: 10.5424/sjar/2014122-4439     Document Type: Article
Times cited : (178)

References (45)
  • 1
    • 0000245743 scopus 로고    scopus 로고
    • Statistical modeling: The two cultures (with discussion)
    • Breiman L, 2001. Statistical modeling: the two cultures (with discussion). Statist Sci 16: 199-231.
    • (2001) Statist Sci , vol.16 , pp. 199-231
    • Breiman, L.1
  • 4
    • 0009530063 scopus 로고
    • Estimating corn yield response models to predict impacts of climate change
    • Dixon BL, Hollinger SE, Garcia P, Tirupattur V, 1994. Estimating corn yield response models to predict impacts of climate change. J Agr Resour Econ 19(1): 58-68.
    • (1994) J Agr Resour Econ , vol.19 , Issue.1 , pp. 58-68
    • Dixon, B.L.1    Hollinger, S.E.2    Garcia, P.3    Tirupattur, V.4
  • 6
    • 80255131393 scopus 로고    scopus 로고
    • Sitespecific early season potato yield forecast by neural network in Eastern Canada
    • Fortin JG, Anctil F, Parent L, Bolinder MA, 2011. Sitespecific early season potato yield forecast by neural network in Eastern Canada. Precis Agr 12(6): 905-923.
    • (2011) Precis Agr , vol.12 , Issue.6 , pp. 905-923
    • Fortin, J.G.1    Anctil, F.2    Parent, L.3    Bolinder, M.A.4
  • 12
    • 33947099470 scopus 로고    scopus 로고
    • Artificial neural network model as a data analysis tool in precision farming
    • Irmak A Jones JW, Batchelor WD, Irmak S, Boote KJ, Paz JO, 2006. Artificial neural network model as a data analysis tool in precision farming. T ASABE 49(6): 2027-2037.
    • (2006) T ASABE , vol.49 , Issue.6 , pp. 2027-2037
    • Irmak, A.1    Jones, J.W.2    Batchelor, W.D.3    Irmak, S.4    Boote, K.J.5    Paz, J.O.6
  • 13
  • 14
    • 0031597355 scopus 로고    scopus 로고
    • Sirius: A mechanistic model of wheat response to environmental variation
    • Jamieson PD, Semenov MA, Brooking IR, Francis GS, 1998a. Sirius: a mechanistic model of wheat response to environmental variation. Eur J Agron 8: 161-179.
    • (1998) Eur J Agron , vol.8 , pp. 161-179
    • Jamieson, P.D.1    Semenov, M.A.2    Brooking, I.R.3    Francis, G.S.4
  • 15
    • 0031984828 scopus 로고    scopus 로고
    • A comparison of the models AFRCWHEAT2, CERES-Wheat, Sirius, SUCROS2 and SWHEAT with measurements from wheat grown under drought
    • Jamieson PD, Porter JR, Goudriaan J, Ritchie JT, van Keulen H, Stol W, 1998b. A comparison of the models AFRCWHEAT2, CERES-Wheat, Sirius, SUCROS2 and SWHEAT with measurements from wheat grown under drought. Field Crops Res 55: 23-44.
    • (1998) Field Crops Res , vol.55 , pp. 23-44
    • Jamieson, P.D.1    Porter, J.R.2    Goudriaan, J.3    Ritchie, J.T.4    van Keulen, H.5    Stol, W.6
  • 18
    • 0035187124 scopus 로고    scopus 로고
    • Neural network for setting target corn yields
    • Liu J Goering CE, Tian L, 2001. Neural network for setting target corn yields. T ASAE 44(3): 705-713.
    • (2001) T ASAE , vol.44 , Issue.3 , pp. 705-713
    • Liu, J.1    Goering, C.E.2    Tian, L.3
  • 22
    • 0002651288 scopus 로고
    • AFRCWHEAT2: A model of the growth and development of wheat incorporating responses to water and nitrogen
    • Porter JR, 1993. AFRCWHEAT2: a model of the growth and development of wheat incorporating responses to water and nitrogen. Eur J Agron 2: 69-82.
    • (1993) Eur J Agron , vol.2 , pp. 69-82
    • Porter, J.R.1
  • 23
  • 24
    • 4544318337 scopus 로고    scopus 로고
    • Factors underlying yield variability in two California rice fields
    • Roel A Plant RE, 2004. Factors underlying yield variability in two California rice fields. Agron J 96: 1481-1494.
    • (2004) Agron J , vol.96 , pp. 1481-1494
    • Roel, A.1    Plant, R.E.2
  • 28
    • 80052257684 scopus 로고    scopus 로고
    • Feature selection for wheat yield prediction
    • Bramer M et al., eds.), Springer-Verlag, London
    • Ruß G Kruse R, 2010. Feature selection for wheat yield prediction. In: Research and development in intelligent systems XXVI (Bramer M et al., eds.), Springer-Verlag, London.
    • (2010) Research and Development In Intelligent Systems XXVI
    • Ruß, G.1    Kruse, R.2
  • 29
    • 84994084511 scopus 로고    scopus 로고
    • Artificial neural networks application to predict wheat yield using climatic data
    • Jan. 10-15, Iranian Meteorological Organization
    • Safa B Khalili A, Teshnehlab M, Liaghat A, 2004. Artificial neural networks application to predict wheat yield using climatic data. Proc. 20th Int. Conf. on IIPS, Jan. 10-15, Iranian Meteorological Organization, pp: 1-39.
    • (2004) Proc. 20th Int. Conf. On IIPS , pp. 1-3
    • Safa, B.1    Khalili, A.2    Teshnehlab, M.3    Liaghat, A.4
  • 32
    • 4043137356 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • Smola A Schölkopf B, 2004. A tutorial on support vector regression. Stat Comput 14(3): 199-222.
    • (2004) Stat Comput , vol.14 , Issue.3 , pp. 199-222
    • Smola, A.1    Schölkopf, B.2
  • 34
    • 0002358501 scopus 로고    scopus 로고
    • Analysis of spatial factors influencing crop yield
    • Robert PC, Rust RH, & Larson WE, eds.) ASA-CSSA-SSSA, Madison, WI, USA
    • Sudduth KA, Drummond ST, Birrell SJ, Kitchen NR, 1996. Analysis of spatial factors influencing crop yield. Proc. 3rd Int. Conf. on Precision Agriculture (Robert PC, Rust RH, & Larson WE, eds.) ASA-CSSA-SSSA, Madison, WI, USA, pp: 129-140.
    • (1996) Proc. 3rd Int. Conf. On Precision Agriculture , pp. 129-140
    • Sudduth, K.A.1    Drummond, S.T.2    Birrell, S.J.3    Kitchen, N.R.4
  • 36
    • 0004679071 scopus 로고    scopus 로고
    • An overview of regression techniques for knowledge discovery
    • Uysal I Altay HG, 1999. An overview of regression techniques for knowledge discovery. Knowl Eng Rev 14: 319-340.
    • (1999) Knowl Eng Rev , vol.14 , pp. 319-340
    • Uysal, I.1    Altay, H.G.2
  • 37
    • 0010864753 scopus 로고
    • Pattern recognition using generalized portrait method
    • Vapnik V Lerner A, 1963. Pattern recognition using generalized portrait method. Automat Remote Contr 24: 774-780.
    • (1963) Automat Remote Contr , vol.24 , pp. 774-780
    • Vapnik, V.1    Lerner, A.2
  • 38
    • 84887252594 scopus 로고    scopus 로고
    • Support vector method for function approximation, regression estimation, and signal processing
    • Mozer M, Jordan M, & Petsche T, eds), MIT Press, Cambridge, MA, USA, pp
    • Vapnik V Golowich S, Smola A, 1997. Support vector method for function approximation, regression estimation, and signal processing. In: Advances in neural information processing systems (Mozer M, Jordan M, & Petsche T, eds), MIT Press, Cambridge, MA, USA, pp: 281-287.
    • (1997) Advances In Neural Information Processing Systems , pp. 281-287
    • Vapnik, V.1    Golowich, S.2    Smola, A.3
  • 39
    • 84984062754 scopus 로고
    • A note on the computer simulation of crop growth in agricultural land evaluation
    • Varcoe VJ, 1990. A note on the computer simulation of crop growth in agricultural land evaluation. Soil Use Manage 6(3): 157-160.
    • (1990) Soil Use Manage , vol.6 , Issue.3 , pp. 157-160
    • Varcoe, V.J.1
  • 40
    • 0001717058 scopus 로고    scopus 로고
    • Inducing model trees for continuous classes
    • (van Someren M & Widmer G, eds)
    • Wang Y Witten I, 1997. Inducing model trees for continuous classes. Proc. 9th Eur. Conf. Machine Learning (van Someren M & Widmer G, eds), pp: 128-137.
    • (1997) Proc. 9th Eur. Conf. Machine Learning , pp. 128-137
    • Wang, Y.1    Witten, I.2
  • 44
    • 39649100346 scopus 로고    scopus 로고
    • Consistency of cross validation for comparing regression procedures
    • Yang Y 2008. Consistency of cross validation for comparing regression procedures. Ann Stat 35 (6): 2450-2473.
    • (2008) Ann Stat , vol.35 , Issue.6 , pp. 2450-2473
    • Yang, Y.1
  • 45
    • 84901828473 scopus 로고    scopus 로고
    • Simulation and prediction of soybean growth and development under field conditions
    • Zhang L Zhang J, Kyei-Boahen S, Zhang M, 2010 Simulation and prediction of soybean growth and development under field conditions. Am-Euras J Agr Environ Sci 7(4): 374-385.
    • (2010) Am-Euras J Agr Environ Sci , vol.7 , Issue.4 , pp. 374-385
    • Zhang, L.1    Zhang, J.2    Kyei-Boahen, S.3    Zhang, M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.