A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT

2016 5th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2016

Volumn , Issue , 2016, Pages

  Stas, Michiel ^a   Van Orshoven, Jos ^a   Dong, Qinghan ^b   Heremans, Stien ^c   Zhang, Beier ^d  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b FLEMISH INSTITUTE FOR TECHNOLOGICAL RESEARCH VITO   (Belgium)

^c RESEARCH INSTITUTE FOR NATURE AND FOREST INBO   (Belgium)

^d Department of Information Science   (China)

Author keywords

Machine Learning; Remote sensing; Wheat yield forecasting

Indexed keywords

ARTIFICIAL INTELLIGENCE; FEATURE EXTRACTION; FORECASTING; FORESTRY; LEARNING SYSTEMS; REMOTE SENSING; SUPPORT VECTOR MACHINES; VEGETATION;

BOOSTED REGRESSION TREES; MACHINE LEARNING METHODS; NDVI TIME SERIES; NORMALIZED DIFFERENCE VEGETATION INDEX; SPOT VEGETATION IMAGERY; WHEAT YIELD; WINTER WHEAT YIELDS; YIELD FORECASTING;

LEARNING ALGORITHMS;

EID: 84994108569     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/Agro-Geoinformatics.2016.7577625     Document Type: Conference Paper

References (30)

1. Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., & Foley, J. A. (2012). Closing yield gaps through nutrient and water management. Nature, 490(7419), 254-257. doi:10. 1038/nature11420
2. Sinclair, T. R., & Rufty, T. W. (2012). Nitrogen and water resources commonly limit crop yield increases, not necessarily plant genetics. Global Food Security, 1(2), 94-98. doi:10. 1016/j. gfs. 2012. 07. 001
3. Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R., & Polasky, S. (2002). Agricultural sustainability and intensive production practices. Nature, 418(6898), 671-677. doi:10. 1038/nature01014
4. Basso, B., Cammarano, D., & Carfagna, E. (2013). Review of Crop Yield Forecasting Methods and Early Warning Systems. The First Meeting of the Scientific Advisory Committee of the Global Strategy to Improve Agricultural and Rural Statistics, 1-56.
5. Barnett, T. L., & Thompson, D. R. (1982). The use of large-area spectral data in wheat yield estimation. Remote Sensing of Environment, 12(6), 509-518. doi:10. 1016/0034-4257(82)90025-6
6. Rasmussen, M. S. (1998). Developing simple, operational, consistent NDVI-vegetation models by applying environmental and climatic information. Part II: Crop yield assessment. International Journal of Remote Sensing Int. J. Remote Sensing, 19(1), 119-139. doi:10. 1080/014311698216468
7. Balaghi, R., Tychon, B., Eerens, H., & Jlibene, M. (2008). Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. International Journal of Applied Earth Observation and Geoinformation, 10(4), 438-452. doi:10. 1016/j. jag. 2006. 12. 001
8. Kogan, F., Kussul, N., Adamenko, T., Skakun, S., Kravchenko, O., Kryvobok, O., . Lavrenyuk, A. (2013). Winter wheat yield forecasting in Ukraine based on Earth observation, meteorologicaldata and biophysical models. International Journal of Applied Earth Observation and Geoinformation, 23(1), 192-203. doi:10. 1016/j. jag. 2013. 01. 002
9. Lopresti, M. F., Di Bella, C. M., & Degioanni, A. J. (2015). Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina. Information Processing in Agriculture, 2(2), 73-84. doi:10. 1016/j. inpa. 2015. 06. 001
10. Rouse, J. W., Jr., Haas, R. H., Schell, J. A., Deering, D. W., 1973. Monitoring vegetation systems in the Great Plains with ETRS. P. 309-317. In Proc. Earth Res. Tech. Satellite-1 Symp., Goddard Space Flight Cent., Washington, DC. 10-14 Dec. 1973.
11. Information Center of the Chinese ministry of agriculture. (2015). Retrieved June 6, 2016, from http://www. agri. cn/province/henan/nyyw/201507/t20150717-4750180. htm
12. Yang, H., & Zehnder, A. (2001). China's regional water scarcity and implications for grain supply and trade. Environment and Planning A, 33(1), 79-95. doi:10. 1068/a3352
13. Royo, C., Aparicio{, N., Villegas{, D., Casadesus{, J., Monneveux, P., & Araus{, J. L. (2003). Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions. International Journal of Remote Sensing, 24(22), 4403-4419. doi:10. 1080/0143116031000150059
14. Heremans, S., Dong, Q., Zhang, B., Bydekerke, L., & Van Orshoven, J. (2015). Potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed normalized difference vegetation index and in situ meteorological data. Journal of Applied Remote Sensing, 9(1), 097095. doi:10. 1117/1. JRS. 9. 097095
15. Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247-259. doi:10. 1016/j. isprsjprs. 2010. 11. 001
16. Swets, D. L., Reed, B. C., Rowland, J. D., & Marko, S. E. (1999). A Weighted Least-Squares Approach to Temporal NDVI Smoothing. In Proceedings of the 1999 ASPRS Annual Conference (pp. 526-536). Portland, Oregon.
17. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. doi:10. 1023/A:1010933404324
18. Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. The Journal of Animal Ecology, 77(4), 802-13. doi:10. 1111/j. 1365-2656. 2008. 01390. x
19. Ridgeway, G. (2015). Package "gbm": Generalized Boosted Regression Models. Retrieved from http://code. google. com/p/gradientboostedmodels/
20. Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297. doi:10. 1023/A:1022627411411
21. Kuhn, M. (n. d. ). The caret Package (Classification And REgression Training).
22. Bolón-Canedo, V., Sánchez-Maronõ, N., & Alonso-Betanzos, A. (2013). A review of feature selection methods on synthetic data. Knowledge and Information Systems, 34(3), 483-519. doi:10. 1007/s10115-012-0487-8
23. Huan Liu, C., Stine, R., & Auslender, L. (2006). Proceedings of the Second Workshop on Feature Selection for Data Mining.
24. Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3), 249-264. doi:10. 1016/S0304-3800(02)00257-0
25. Eugster, M., Hothorn, T., & Leisch, F. (2012). Domain-Based Benchmark Experiments: Exploratory and Inferential Analysis. Austrian Journal of Statistics, 41(1), 5-26. Retrieved from http://www. stat. tugraz. at/AJS/ausg121/121Leisch
26. Hothorn, T., Leisch, F., Zeileis, A., Hornik, K., Hothorn, T., Leisch, F., . Hornik, K. (2005). The Design and Analysis of Benchmark Experiments. Journal of Computational and Graphical Statistics, 14(3), 675-699. doi:10. 1198/106186005X59630
27. Erdal, H. I., & Karakurt, O. (2013). Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms. Journal of Hydrology, 477, 119-128. doi:10. 1016/j. jhydrol. 2012. 11. 015
28. Heremans, S., & Van Orshoven, J. (2015). Machine learning methods for sub-pixel land-cover classification in the spatially heterogeneous region of Flanders (Belgium): a multi-criteria comparison. International Journal of Remote Sensing, 36(11), 2934-2962. doi:10. 1080/01431161. 2015. 1054047
29. Pouteau, R., Collin, A., & Stoll, B. (n. d. ). A Comparison of Machine Learning Algorithms for Classification of Tropical Ecosystems Observed by Multiple Sensors at Multiple Scales.
30. Huang, J., Wang, X., Li, X., Tian, H., Pan, Z., Prasad, A., . Cutler, M. (2013). Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR. PLoS ONE, 8(8), e70816. doi:10. 1371/journal. pone. 0070816