International Journal of Plant Production

, Volume 13, Issue 1, pp 11–22 | Cite as

Comparison of Data Mining and GDD-Based Models in Discrimination of Maize Phenology

  • Mahdi Ghamghami
  • Nozar GhahremanEmail author
  • Parviz Irannejad
  • Khalil Ghorbani


Data mining approaches are designed for classification problems in which each observation is a member of one and only one class. In this study, a non-deterministic approach based on C5.0 data mining algorithm has been employed for discriminating the phenological stages of maize from emergence to dough, in a field located in Karaj, Iran. Two readily-available predictors i.e. accumulated growing degree days (AGDD) and multi-temporal LANDSAT7-extracted normalized difference vegetation index (NDVI) was used to build the decision tree. The AGDD was calculated based on three cardinal thresholds of temperature i.e. effective minimum, optimum, effective maximum. The NDVI was compared with two recently developed indices namely, enhanced vegetation index2 (EVI2) and optimized soil adjusted vegetation index (OSAVI) using the signal to noise ratio (SNR) criterion. Findings confirmed that these three remotely sensed indices do not have significant differences, therefore, the smoothed time series of NDVI was used in the C5.0 algorithm. The precisions of classification by C5.0 data mining algorithm in partitioning of training and testing data were approximately 90.51 and 81.77%, respectively. The mean absolute error (MAE) values of the onset of maize phenological stages were estimated about 2.6–5.3 days for various stages by C5.0 model. While corresponding values for the classical AGDD model were 3.9–10.7 days. This confirms the skill of data mining approach in comparison with commonly-used the classical AGDD model in applications of real time monitoring.


NDVI AGDD Phenology model C5.0 



The authors are grateful to the Alborz Province office of the Iranian Meteorological Organization for providing the phenological data.


  1. Arvor, D., Jonathan, M., Meirelles, M. S. P., Dubreuil, V., & Lecerf, R. (2008). Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring. In Geoscience and Remote Sensing Symposium. IGARSS 2008. IEEE International, 2, 958–961.Google Scholar
  2. Baskerville, G. L., & Emin, P. (1969). Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology, 50, 514–517.CrossRefGoogle Scholar
  3. Brown, M. E., de Beurs, K. M., & Marshall, M. (2012). Global phenological response to climate change in crop areas using satellite remote sensing of vegetation, humidity and temperature over 26 years. Remote Sensing of Environment, 126, 174–183.CrossRefGoogle Scholar
  4. Cassiane, J. M., Nelson, M., Petry, M. T., Carlesso, R., Silveria Kersten, D., Basso, L., et al. (2015). Using NDVI time series profiles for monitoring corn plant phenology of irrigated areas in Southern Brazil. Agrociencia, 19(3), 79. (only abstract).Google Scholar
  5. Craufurd, P. Q., & Wheeler, T. R. (2009). Climate change and the flowering time of annual crops. Journal of Experimental Botany, 60(9), 2529–2539.CrossRefGoogle Scholar
  6. Curnel, Y., Oger, R. (2007). Agrophenology indicators from remote sensing: state of the art. In: ISPRS Archives XXXVI-8/W48 Workshop proceedings: Remote sensing support to crop yield forecast and area estimates.Google Scholar
  7. Dash, J., Lankester, T., Hubbard, S., Curran, P.J. (2008). Signal to noise ratio for MTCI and NDVI time series data. In: Proceedings of the 2nd MERIS/(A)ATSR User Workshop, Frascati, Italy, 22–26 September.Google Scholar
  8. Davidson, A., & Csillag, F. (2003). A comparison of three approaches for predicting C4 species cover of northern mixed grass prairie. Remote Sensing of Environment, 86, 70–82.CrossRefGoogle Scholar
  9. De Beurs, K. M., & Henebry, G. M. (2010). Spatio-temporal statistical methods for modelling land surface phenology. In I. L. Hudson & M. R. Keatley (Eds.), Phenological research: Methods for environmental and climate change analysis. New York: Springer-Verlag.Google Scholar
  10. Diepen, C. A., Wolf, J., & van Keulen, H. (1989). WOFOST: A simulation model of crop production. Soil Use Management, 5, 16–24.CrossRefGoogle Scholar
  11. Dwyer, L. M., Stewart, D. W., Carrigan, L., Neave, B. L., Ma, P., & Balchin, D. (1999a). A general thermal index for maize. Agronomy Journal, 91, 946–949.CrossRefGoogle Scholar
  12. Dwyer, L. M., Stewart, D. W., Carrigan, L., Neave, B. L., Ma, P., & Balchin, D. (1999b). Guidelines for comparisons among different maize maturity rating systems. Agronomy Journal, 91, 946–949.CrossRefGoogle Scholar
  13. Ghahreman, N., & Sameti, M. (2014). Comparison of M5 model tree and artificial neural network for estimating potential evapotranspiration in semi-arid climates. Desert, 19(1), 75–81.Google Scholar
  14. Hill, M. G., Connolly, P. G., Reutemann, P., & Fletcher, D. (2014). The use of data mining to assist crop protection decisions on kiwifruit in New Zealand. Computers and Electronic in Agriculture, 108, 250–257.CrossRefGoogle Scholar
  15. Hmimina, G., Dufrêne, E., Pontailler, J. Y., Delpierre, N., Aubinet, M., & Caquet, B. (2013). Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements. Remote Sensing of Environment, 132, 145–158.CrossRefGoogle Scholar
  16. Huete, A. R., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing Environment, 83(1–2), 195–213.CrossRefGoogle Scholar
  17. Hufkens, K., Friedl, M., Sonnentag, O., Braswell, B. H., Milliman, T., & Richardson, A. D. (2012). Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sensing of Environment, 117, 307–321.CrossRefGoogle Scholar
  18. Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-ban enhanced vegetation index without a blue band. Remote Sensing Environment, 112, 3833–3845.CrossRefGoogle Scholar
  19. Jones, J. W., Tsuji, G. Y., Hoogenboom, G., Hunt, L. A., Thornton, P. K., Wilkens, P. W., et al. (1998). Decision support system for agrotechnology transfer : DSSAT V3. In G. Y. Tsuji, G. Hoogenboom, & P. Thornton (Eds.), Understanding options for agricultural production (pp. 157–177). Boston: Kluwer Academic Publishers.CrossRefGoogle Scholar
  20. Kamble, B., Kilic, A., & Hubbard, K. (2013). Estimating crop coefficients using remote sensing-based vegetation index. Remote Sensing, 5, 1588–1602.CrossRefGoogle Scholar
  21. Kheir, B., Greve, M. H., Bøcher, P. K., Greve, M. B., Larsen, R., & McCloy, K. (2010). Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: The case study of Denmark. Journal of Environmental Management, 91, 1150–1160.CrossRefGoogle Scholar
  22. Klisch, A., Royer, C., Lazar, B., Baruth, G. (2006). Extraction of phenological parameteres from temporally smoothed vegetation indices. In: ISPRS WG VIII/10 Workshop 2006 remote sensing support to crop yield forecast and area estimates November 30–December 1, 2006 Stresa, Italy, 91-96.Google Scholar
  23. Kroes, J. G., Dam, J. C. V., Groenendijk, P., Hendriks, R. F. A., & Jacobs, C. M. J. (2008). SWAP Version 3.2: theory description and user manual. Alterra Report; Alterra. Wageningen: Alterra.Google Scholar
  24. Kumudini, S., Andrade, F., Boote, K., Brown, G., Dzotsi, K., Edmeades, G., et al. (2014). Predicting maize phenology: intercomparison of functions for developmental response to temperature. Agronomy Journal, 106(6), 2087–2097.CrossRefGoogle Scholar
  25. Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer, 600 ppGoogle Scholar
  26. Li, Z., Huffman, T., Zhang, A., Zhou, F., & McConkey, B. (2012). Spatially locating soil classes within complex soil polygons—mapping soil capability for agriculture in Saskatchewan Canada. Agriculture, Ecosystems & Environment, 152(5), 59–67.CrossRefGoogle Scholar
  27. Li, Q., Wang, C., Zhang, B., & Lu, L. (2015). Object-based crop classification with landsat-MODIS enhanced time-series data. Remote Sensing, 7, 16091–16107.CrossRefGoogle Scholar
  28. Liu, J., Patty, E., & Jego, G. (2012). Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sensing of Environment, 123, 347–358.CrossRefGoogle Scholar
  29. Londhe, T., & Dixit, M. (2011). Stream flow forecasting using model trees. International Journal of Earth Science Engineering, 4(6), 282–285.Google Scholar
  30. Mavi, H. S., & Tupper, G. J. (2004). Agrometeorology—principles and applications of climate studies in agriculture (pp. 43–70). Haworth: Press Binghamton.CrossRefGoogle Scholar
  31. McMaster, G. S., & Smika, D. E. (1988). Estimation and evaluation of winter wheat phenology in the central Great Plains. Agriculture and Forest Meteorology, 43, 1–18.CrossRefGoogle Scholar
  32. Mitchell T.M. (1997). Machine Learning. McGraw-Hill International.Google Scholar
  33. Pandya, R., & Pandya, J. (2015). C5.0 algorithm to improved decision tree with feature selection and reduced error pruning. International Journal of Computer Applications, 117(16), 18–21.CrossRefGoogle Scholar
  34. Pena-Barragan, J., Ngugi, M. K., Plant, R. E., & Six, J. (2011). Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 115, 1301–1316.CrossRefGoogle Scholar
  35. Quinlan, J. R. (1993). C4.5: Programs for machine learning. Burlington, USA: Morgan Kaufmann Publishers.Google Scholar
  36. Ritchie, J. T., & NeSmith, D. S. (1991). Temperature and crop development. In R. J. Hanks & J. T. Ritchie (Eds.), Modeling plant and soil systems. Monograph (Vol. 31, pp. 5–29). Madison: American Society of Agronomy.Google Scholar
  37. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107.CrossRefGoogle Scholar
  38. Roth, G. W., & Yocum, J. O. (1997). Use of hybrid growing degree day ratings for corn in the northeastern USA. Journal of Production Agriculture, 10, 283–288.CrossRefGoogle Scholar
  39. Sakamoto, T., Wardlow, B. D., Gitelson, A. A., Verma, S. B., Suyker, A. E., & Arkebauer, T. J. (2010). A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sensing of Environment, 114, 2146–2159.CrossRefGoogle Scholar
  40. Saxton, K. E., Porterand, M. A., & McMahon, T. A. (1992). Climatic impacts on dryland winter wheat by daily soil water and crop stress simulations. Agriculture and Forest Meteorology, 58, 177–192.CrossRefGoogle Scholar
  41. Shen, Y., Di, L., Wu, L., Yu, G., Tang, H., Yu, G., et al. (2013). Hidden Markov Models for real-time estimation of corn progress stages using MODIS and meteorological data. Remote Sensing, 5, 1734–1753.CrossRefGoogle Scholar
  42. Stöckle, C. O., Donatelli, M., & Nelson, R. (2003). Cropsyst, a cropping systems simulation model. European Journal of Agronomy, 18, 289–307.CrossRefGoogle Scholar
  43. Stowe, L., Davis, P. A., & McClain, E. P. (1999). Scientific basis and initial evaluation of the CLAVR-1 Global Clear/cloud classification algorithm for the advanced very high resolution radiometer. Journal of Atmospheric and Oceanic Technology, 16(6), 656–681.CrossRefGoogle Scholar
  44. Streck, N. A., Lago, I., Gabriel, L. F., & Samboranha, F. K. (2008). Simulating maize phenology as a function of air temperature with a linear and a nonlinear model. Pesquisa Agropecuária Brasileira, 43, 449–455.CrossRefGoogle Scholar
  45. Swets, D.L., Reed, B.C., Rowland, J.D., Marko, S.E. (1999). a weighted least-squares approach to temporal NDVI smoothing. In: Proceedings Amr. Soc. Photogram. Rem. Sens. 17–21 May, Portland OR., ASPRS, Washington, DC, pp 526–536.Google Scholar
  46. Teal, R. K., Tubana, B. S., Girma, K., Freeman, K. W., Arnall, D. B., Walsh, O., et al. (2006). In-season prediction of corn grain yield potential using normalized difference vegetation index. Agronomy Journal, 98, 1488–1494.CrossRefGoogle Scholar
  47. Tsimba, R., Edmeades, G. O., Millner, J. P., & Kemp, P. D. (2013). The effect of planting date on maize grain yields an yield components. Field Crops Research, 150, 135–144.CrossRefGoogle Scholar
  48. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.CrossRefGoogle Scholar
  49. Van Dijk, A., Callis, S. L., Sakamoto, C. M., & Decker, W. L. (1985). Smoothing vegetation index profiles: an alternative method for reducing radiometric disturbance in NOAA/AVHRR data. Photogram Engineering Remote Sensing, 53, 1059–1067.Google Scholar
  50. Viña, A., Gitelson, A. A., Rundquist, D. C., Keydan, G., Leavitt, B., & Schepers, J. (2004). Monitoring maize (Zea mays L.) phenology with remote sensing. Agronomy Journal, 96, 1139–1147.CrossRefGoogle Scholar
  51. Viovy, N., Arino, O., & Belward, A. S. (1992). The best index slope extraction (BISE): a method for reducing noise in NDVI time series. International Journal of Remote Sensing, 13(8), 1585–1590.CrossRefGoogle Scholar
  52. Wang, J. Y. (1960). A critique of the heat unit approach to plant response studies. Ecology, 41, 785–790.CrossRefGoogle Scholar
  53. White, K., Pontius, J., & Schaberg, P. (2014). Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty. Remote Sensing of Environment, 148, 97–107.CrossRefGoogle Scholar
  54. Wu, C., Gonsamo, A., Gough, C. M., Chen, J. M., & Xu, S. (2014). Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS. Remote Sensing of Environment, 147, 79–88.CrossRefGoogle Scholar
  55. Zhang, X., Friedl, M., Schaaf, M., Strahler, A. H., Hodges, J. C. F., Gao, F., et al. (2003). A monitoring vegetation phenology using MODIS. Remote Sensing Environment, 84, 471–475.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Irrigation and Reclamation EngineeringUniversity College of Agriculture and Natural Resources, University of TehranKarajIran
  2. 2.Geophysics InstituteUniversity of TehranTehranIran
  3. 3.Department of Water EngineeringGorgan University of Agricultural Science and Natural ResourcesGorganIran

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