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Prediction Models for Crop Mapping

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Part of the Studies in Big Data book series (SBD, volume 72)

Abstract

Crops are plants that are grown for food. Unless the available productive land which is the main source of human sustenance is protected and used in a scientific way to give better and increased returns there is no hope of human survival. The following chapter focuses on effective implementation of prediction algorithms for crop cover mapping. The various methods that have been, or could be used for crop cover mapping are discussed. The potential indicators that have been, or could be, used in crop prediction modelling are also discussed.

References

  1. An, Q., & Yang, B. (2007). A multicrop identification model based on stepwise removal learning-support vector machine using remote sensing images. New Zealand Journal of Agricultural Research, 50(5), 1013–1019.CrossRefGoogle Scholar
  2. Arenas-Toledo, J. M., & Epiphanio, J. C. N. (2011). Harmonic amplitude-terms mask to highlight agriculture in the savanna domain below the Brazilian Amazonian frontier. International Journal of Remote Sensing, 32(18), 5021–5034.CrossRefGoogle Scholar
  3. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.zbMATHGoogle Scholar
  4. Breiman, L. (1997). Arcing the edge. Statistics, 4, 1–14.Google Scholar
  5. Breiman, L. (1998). Arcing classifier (with discussion and a rejoinder by the author). The Annals of Statistics, 26(3), 801–849.MathSciNetzbMATHCrossRefGoogle Scholar
  6. Breiman, L. (2001). Randomforest. Machine Learning, 45(1), 5–32.CrossRefGoogle Scholar
  7. Breiman, L., Friedman, J. H., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Monterey, California, USA: Wadsworth and Brooks/Cole.zbMATHGoogle Scholar
  8. Brisco, B., Brown, R. J., Hirose, T., McNairn, H., & Staenz, K. (1998). Precision agriculture and the role of remote sensing: A review. Canadian Journal of Remote Sensing, 24(3), 315–327.CrossRefGoogle Scholar
  9. ChenChi, F., ChenCheng, R., & Son Nguyen, T. (2012). Investigating rice cropping practices and growing areas from MODIS data using empirical mode decomposition and support vector machines. GIScience & Remote Sensing, 49(1), 117–138.CrossRefGoogle Scholar
  10. Deschamps, B., McNairn, H., Shang, J., & Jiao, X. (2012). Towards operational radar-only crop type classification: Comparison of a traditional decision tree with a random forest classifier. Canadian Journal of Remote Sensing, 38(1), 60–68.CrossRefGoogle Scholar
  11. Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees. Machine Learning, 40, 139–157.CrossRefGoogle Scholar
  12. Freund, Y., & Schapire, R. E. (1996). Experiments with a New Boosting Algorithm. In L. Saitta (ed.), Proceedings of the Thirteenth International Conference on Machine Learning (pp. 148–165). Bari, Italy: Morgan Kaufmann.Google Scholar
  13. Freund, Y., & Schapire, R. E. (1997). A desicion-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119–139.MathSciNetzbMATHCrossRefGoogle Scholar
  14. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.MathSciNetzbMATHCrossRefGoogle Scholar
  15. Friedman, J. H., & Popescu, B. E. (2003). Importance sampled learning ensembles. Computing, 94305(2), 1–32.Google Scholar
  16. Friedman, J. H., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics, 28(2), 337–407.MathSciNetzbMATHCrossRefGoogle Scholar
  17. Genuer, R., Poggi, J., & Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recognition Letters, 31(14), 2225–2236.CrossRefGoogle Scholar
  18. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426.CrossRefGoogle Scholar
  19. Harris, R. (2003). Remote sensing of agriculture change in Oman. International Journal of Remote Sensing, 24(23), 4835–4852.CrossRefGoogle Scholar
  20. Jaccard, P. (1912). The distribution of the flora in the alpine zone. New Phytologist, 11(2), 37–50.CrossRefGoogle Scholar
  21. Kumar, P., Gupta, D. K., Mishra, V. N., & Prasad, R. (2015). Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data. International Journal of Remote Sensing, 36(6), 1604–1617.CrossRefGoogle Scholar
  22. Kumar, P., Prasad, R., Choudhary, A., Mishra, V. N., Gupta, D. K., & Srivastava, P. K. (2016). A statistical significance of differences in classification accuracy of crop types using different classification algorithms. Geocarto International, 6049(May), 1–19.CrossRefGoogle Scholar
  23. Labatut, V., & Cherifi, H. (2011). Evaluation of performance measures for classifiers comparison. Ubiquitous Computing and Communication Journal, 6, 21–34.Google Scholar
  24. Labatut, V., & Cherifi, H. (2012). Accuracy measures for the comparison of classifiers. In The 5th International Conference on Information Technology (p. 11). Amman, Jordan.Google Scholar
  25. Liaw, A., & Wiener, M. (2002). Classification and Regression by Random Forest, 2(3), 18–22.Google Scholar
  26. Mathur, A., & Foody, G. M. (2008). Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 29(8), 2227–2240.CrossRefGoogle Scholar
  27. Mellor, A., Haywood, A., Stone, C., & Jones, S. (2013). The performance of random forests in an operational setting for large area Sclerophyll forest classification. Remote Sensing, 5(6), 2838–2856.CrossRefGoogle Scholar
  28. Metternicht, G. (2003). Vegetation indices derived from high-resolution airborne videography for precision crop management. International Journal of Remote Sensing, 24(14), 2855–2877.CrossRefGoogle Scholar
  29. Murthy, C. S., Raju, P. V., & Badrinath, K. V. S. (2003). Classification of wheat crop with multi-temporal images: Performance of maximum likelihood and artificial neural networks. International Journal of Remote Sensing, 24(23), 4871–4890.CrossRefGoogle Scholar
  30. Punera, K., & Ghosh, J. (2008). Consensus-based ensembles of soft clusterings. Applied Artificial Intelligence, 22(7–8), 780–810.CrossRefGoogle Scholar
  31. Rätsch, G., Onoda, T., & Müller, K. R. (2001). Soft margins for AdaBoost. Machine Learning, 42(3), 287–320.zbMATHCrossRefGoogle Scholar
  32. Richards, J. A. (2013). Interpreting images. In Remote sensing digital image analysis (pp. 79–97). Berlin, Heidelberg: Springer Berlin Heidelberg.Google Scholar
  33. Rilwani, M. L., & Ikhuoria, I. A. (2011). Prospects for geoinformatics-based precision farming in the Savanna River basin Nigeria. International Journal of Remote Sensing, 32(12), 3539–3549.CrossRefGoogle Scholar
  34. Rodriguez-Galiano, V. F., & Chica-Rivas, M. (2014). Evaluation of different machine learning methods for land cover mapping of a mediterranean area using multi-seasonal landsat images and digital terrain models. International Journal of Digital Earth, 7(6), 492–509.CrossRefGoogle Scholar
  35. Sarkar, A., Majumdar, A., Chatterjee, S., Chatterjee, D., Ray, S., & Kartikeyan, B. (2008). Study of the potential of alternative crops by integration of multisource data using a neuro-fuzzy technique. International Journal of Remote Sensing, 29(19), 5479–5493.CrossRefGoogle Scholar
  36. Seni, G., & Elder, J. F. (2010). Ensemble methods in data mining: Improving accuracy through combining predictions. Synthesis Lectures on Data Mining and Knowledge Discovery.Google Scholar
  37. Sonobe, R., Tani, H., Wang, X., Kobayashi, N., & Shimamura, H. (2014). Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-X. International Journal of Remote Sensing, 35(23), 7898–7909.CrossRefGoogle Scholar
  38. Sonobe, R., Tani, H., & Wang, X. (2016). An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data. Geocarto International, 32(2), 1–11.CrossRefGoogle Scholar
  39. Steinberg, D., & Colla, P. (1995). CART: Tree-structured non-parametric data analysis. San Diego, CA: Salford SystemsGoogle Scholar
  40. Tan, C. P., Ewe, H. T., & Chuah, H. T. (2011). Agricultural crop-type classification of multi-polarization SAR images using a hybrid entropy decomposition and support vector machine technique. International Journal of Remote Sensing, 32(22), 7057–7071.CrossRefGoogle Scholar
  41. Tumer, K., & Oza, N. C. (2003). Input decimated ensembles. Pattern Analysis and Applications, 6(1), 65–77.MathSciNetzbMATHCrossRefGoogle Scholar
  42. Wang, X., Liang, T., Xie, H., Huang, X., & Lin, H. (2016). Climate-driven changes in grassland vegetation, snow cover, and lake water of the Qinghai Lake basin. Journal of Applied Remote Sensing, 10(3), 036017.CrossRefGoogle Scholar
  43. Weigend, A.S., Mangeas, M., & Srivastava, A. N. (1995). Nonlinear gated experts for time series: Discovering regimes and avoiding overfitting. International Journal of Neural Systems, 6(4), 373–399.Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Geomatics Section, Civil Engineering DepartmentIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Surveying and Geomatics Section, Civil Engineering DepartmentMaharishi Markandeshwar UniversityAmbalaIndia
  3. 3.Indian Institute of Remote Sensing (IIRS)DehradunIndia

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