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7 Data Fusion in Agricultural Information Systems
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evaluation of SIFT and convolutional. International Journal of Advanced Computer Science
and Applications, 8, 12.
Sarmadian, F., & Mehrjardi, R. T. (2008). Modeling of some soil properties using artificial neural
network and multivariate regression in Gorgan Province, North of Iran. Global Journal of
Environmental Research, 2, 30–35.
Schröder, W., Schmidt, G., & Schönrock, S. (2014). Modelling and mapping of plant phenological
stages as bio-meteorological indicators for climate change. Environmental Sciences Europe, 26
(5), 1–13.
Smith, L., Turcotte, D., & Isacks, B. (1998). Stream flow characterization and feature detection
using a discrete wavelet transform. Hydrological Processes, 12(2), 233–249. https://doi.org/10.
1002/(SICI)1099-1085(199802)12:2<233::AID-HYP573>3.0.CO;2-3.
Sweldens, W. (1997). “The lifting scheme: A construction of second generation wavelets” (PDF).
Journal on Mathematical Analysis, 29(2), 511–546. https://doi.org/10.1137/
S0036141095289051.
Tokar, A. S., & Markus, M. (2000). Precipitation-runoff modeling using artificial neural networks
and conceptual models. Journal of Hydrologic Engineering.
Üstündağ, B. B. (2017, February). An adaptive mealy machine model for monitoring crop status.
Journal of Integrative Agriculture, Remote Sensing Special Issue, 16(2), 252–265.
White, F. E. (1991). JDL, data fusion lexicon, technical panel for C3, F.E. White, San Diego, Calif,
USA, Code 420.
World Bank. (2019). data.worldbank.com. https://data.worldbank.org/indicator/ag.lnd.arbl.ha.pc
Zhang, T., Ye, S., Zhang, K., Tang, J., Wen, W., Fardad, M., & Wang, Y. (2018). A systematic
DNN weight pruning framework using alternating direction method of multipliers. European
Conference on Computer Vision – ECCV 2018, pp 191–207, Springer, Lecture notes in
Computer Science. LNCS, 11, 212.
7 Data Fusion in Agricultural Information Systems
141