service-oriented software development for smartphone and IoT applications as well
as large-scale management systems.
Acknowledgments This work was supported by the Republic of Turkey Ministry of Development
within Agricultural Monitoring and Information Systems Project (TARBIL, Pr.no:2011A020090).
Appendix
References
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-guidelines for
computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300
(9), 1998, p. D05109.
Altan, M. T., & Üstündağ, B. B. (2012). Reconstruction of missing meteorological data using
wavelet transform. IEEE First Agro-geoinformatics conference, Shanghai. https://doi.org/10.
1109/Agro-Geoinformatics.2012.6311644.
Amrawat, T., Solanki, N. S., Sharma, S. K., Jajoria, D. K., & Dotaniya, M. L. (2013). Phenology
growth and yield of wheat in relation to agrometeorological indices under different sowing
dates. African Journal of Agricultural Research, 8(49), 6366–6374. https://doi.org/10.5897/
AJAR2013.8019.
Bagis, S., & Üstündağ, B. B. (2012). Image based automated phenological stage detection of cereal
plants. Agro-geoinformatics conference, Shanghai. https://doi.org/10.1109/AgroGeoinformatics.2012.6311643.
Bagis, S., Üstündağ, B. B., & Ozelkan, E. (2012). An adaptive spatiotemporal agricultural cropland
temperature prediction system based on ground and satellite measurements. In First Agrogeoinformatics conference, Shanghai. https://doi.org/10.1109/Agro-Geoinformatics.2012.
6311642
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, Elsevier, 10,
438–452. https://doi.org/10.1016/j.jag.2006.12.001.
Bazgeera, S., Kamalib, G., & Mortazavic, A. (2007). Wheat yield prediction through
agrometeorological indices for Hamedan, Iran. BIABAN (Desert Journal), 12, 33–38.
Burrus, C. S., Gopinath, R. A., & Guo, H. (1998). Introduction to wavelets and wavelet transforms:
A primer. Prentice-Hall.
Castandeo, F. (2013). A review of data fusion techniques. The Scientific World Journal, 2013,
Article ID 704504, 19 pages, https://doi.org/10.1155/2013/704504. Hindawi Publishing
Corporation
Dong, C., Hu, D., Fu, Y., Wang, M., & Liu, H. (2014). Analysis and optimization of the effect of
light and nutrient solution on wheat growth and development using an inverse system model
strategy. Computers and Electronics in Agriculture, 109, 221–234. https://doi.org/10.1016/j.
compag.2014.10.013.
Donoho, D. L. (1993). Unconditional bases are optimal bases for data compression and for
statistical estimation. Applied and Computational Harmonic Analysis, 1(1), 1008211;115.
Also Stanford Statistics Dept. Report TR-410, Nov. 1992.
Donoho, D. L., Johnstone, I. M., Kerkyacharian, G., & Picard, D. (1995). Wavelet shrinkage:
Asymptopia? Journal Royal Statistical Society B, 57(2), 3018211;337. Also Stanford Statistics
Dept. Report TR-419, March 1993.
7 Data Fusion in Agricultural Information Systems
139
as large-scale management systems.
Acknowledgments This work was supported by the Republic of Turkey Ministry of Development
within Agricultural Monitoring and Information Systems Project (TARBIL, Pr.no:2011A020090).
Appendix
References
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-guidelines for
computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300
(9), 1998, p. D05109.
Altan, M. T., & Üstündağ, B. B. (2012). Reconstruction of missing meteorological data using
wavelet transform. IEEE First Agro-geoinformatics conference, Shanghai. https://doi.org/10.
1109/Agro-Geoinformatics.2012.6311644.
Amrawat, T., Solanki, N. S., Sharma, S. K., Jajoria, D. K., & Dotaniya, M. L. (2013). Phenology
growth and yield of wheat in relation to agrometeorological indices under different sowing
dates. African Journal of Agricultural Research, 8(49), 6366–6374. https://doi.org/10.5897/
AJAR2013.8019.
Bagis, S., & Üstündağ, B. B. (2012). Image based automated phenological stage detection of cereal
plants. Agro-geoinformatics conference, Shanghai. https://doi.org/10.1109/AgroGeoinformatics.2012.6311643.
Bagis, S., Üstündağ, B. B., & Ozelkan, E. (2012). An adaptive spatiotemporal agricultural cropland
temperature prediction system based on ground and satellite measurements. In First Agrogeoinformatics conference, Shanghai. https://doi.org/10.1109/Agro-Geoinformatics.2012.
6311642
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, Elsevier, 10,
438–452. https://doi.org/10.1016/j.jag.2006.12.001.
Bazgeera, S., Kamalib, G., & Mortazavic, A. (2007). Wheat yield prediction through
agrometeorological indices for Hamedan, Iran. BIABAN (Desert Journal), 12, 33–38.
Burrus, C. S., Gopinath, R. A., & Guo, H. (1998). Introduction to wavelets and wavelet transforms:
A primer. Prentice-Hall.
Castandeo, F. (2013). A review of data fusion techniques. The Scientific World Journal, 2013,
Article ID 704504, 19 pages, https://doi.org/10.1155/2013/704504. Hindawi Publishing
Corporation
Dong, C., Hu, D., Fu, Y., Wang, M., & Liu, H. (2014). Analysis and optimization of the effect of
light and nutrient solution on wheat growth and development using an inverse system model
strategy. Computers and Electronics in Agriculture, 109, 221–234. https://doi.org/10.1016/j.
compag.2014.10.013.
Donoho, D. L. (1993). Unconditional bases are optimal bases for data compression and for
statistical estimation. Applied and Computational Harmonic Analysis, 1(1), 1008211;115.
Also Stanford Statistics Dept. Report TR-410, Nov. 1992.
Donoho, D. L., Johnstone, I. M., Kerkyacharian, G., & Picard, D. (1995). Wavelet shrinkage:
Asymptopia? Journal Royal Statistical Society B, 57(2), 3018211;337. Also Stanford Statistics
Dept. Report TR-419, March 1993.
7 Data Fusion in Agricultural Information Systems
139