1 |
Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M., and Notarnicol, C. (2015), Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data, Remote Sensing, Vol. 7, No. 12, pp. 16398-16421.
DOI
|
2 |
Ali, J., Khan, R., Ahmad, N., and Maqsood, I. (2012), Random forests and decision trees, International Journal of Computer Science Issues, Vol. 9, No. 5, pp. 272-278.
|
3 |
Breiman, L. (2001), Random forests, Machine Learning, Vol. 45, No. 1, pp. 5-32.
DOI
|
4 |
Cortes, C. and Vapnik, V. (1995), Support-vector network, Machine Learning, Vol. 20, No. 3, pp. 273-297.
DOI
|
5 |
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., and Vincent, P. (2010), Why does unsupervised pre-training help deep learning?, Journal of Machine Learning Research, Vol. 11, pp. 625-660.
|
6 |
Friedman, J.H. (1997), On bias, variance, 0/1-loss, and the curse-of-dimensionality, Data Mining and Knowledge Discovery, Vol. 1, pp. 55-77.
DOI
|
7 |
Geurts, P., Ernst, D., and Wehenkel, L. (2006), Extremely randomized trees, Machine Learning, Vol. 63, No. 1, pp. 3-42.
DOI
|
8 |
Hong, S.Y., Na, S.I., Lee, K.D., Kim, Y.S., and Baek, S.C. (2015), A study on estimating rice yield in DPRK using MODIS NDVI and rainfall data, Korean Journal of Remote Sensing, Vol. 31, No. 5, pp. 441-448. (in Korean with English abstract)
DOI
|
9 |
Jaikla, R., Auephanwiriyakul, S., and Jintrawet, A. (2008), Rice yield prediction using a support vector regression method, Proceedings of Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology 2008, 14-17 May, Krabi, Thailand, pp. 908-913.
|
10 |
Jiang, D., Yango, X., Clinton, N., and Wang, N. (2004), An artificial neural network model for estimating crop yields using remotely sensed information, International Journal of Remote Sensing, Vol. 25, No. 9, pp. 1723-1732.
DOI
|
11 |
Karatzoglou, A., Meyer, D., and Hornik, K. (2006), Support vector machines in R, Journal of Statistical Software, Vol. 15, No. 9. pp. 1-28.
|
12 |
Kim, N., Cho, J., Shibasaki, R., and Lee, Y.W. (2014), Estimation of corn and soybean yields of the US Midwest using satellite imagery and climate dataset, Journal of Climate Research, Vol. 9, No. 4, pp. 315-329. (in Korean with English abstract)
DOI
|
13 |
Ren, J.Q., Chen, Z.X., Zhou, Q.B., and Tang, H.J. (2008), Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, Vol. 10, pp. 403-413.
DOI
|
14 |
Kuwata, K. and Shibasaki, R. (2015), Estimating crop yields with deep learning and remotely sensed data, Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium, 26-31 July, Milan, Italy, pp. 858-861.
|
15 |
Na, S., Hong, S., Kim, Y., and Lee, K. (2014), Estimation of corn and soybean yields based on MODIS data and CASA model in Iowa and Illinois, USA, Korean Journal of Soil Science and Fertilizer, Vol. 47, No. 2, pp. 92-99. (in Korean with English abstract)
DOI
|
16 |
Prasad, A.K., Chai, L., Singh, R.P., and Kafatos, M. (2006), Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, Vol. 8, pp. 26-33.
DOI
|
17 |
USDA (2012), Census of agriculture, United States Department of Agriculture, https://www.agcensus.usda.gov/ (last date accessed: 17 August 2016).
|
18 |
Vapnik, V. (1998), Statistical Learning Theory, Wiley, New York, NY.
|