شناسایی رنگ و بافت خاک با استفاده از الگوریتم هوش مصنوعی و تصاویر ماهواره ای

Agussabti R., Purwana S., and Agus A.M. 2020. Data analysis on near infrared spectroscopy as a part of technology adoption for cocoa farmer in Aceh Province, Indonesia, Data in brief 29 105251, 1-9.

Asadzadeh F., Khosraviaqdam K., Yaghmaeian Mahabadi N., and Ramezanpour H. 2019. Spatial variation of mineral particles of the soil using remote sensing data and geostatistics to the soil texture interpolation. Journal of Water and Soil, 32(6): 1207-1222.

Asgari N., Ayoubi SH., Dematte J.A.M., Jafari A., Safanelli J.L., and Silveira D.D. 2020. Digital mapping of soil drainage using remote sensing, DEM and soil color in a semiarid region of Central Iran. Geoderma Regional, 22(e00302): 1-10.

Barman U., and Choudhury R.D. 2020. Soil texture classification using multi class support vector machine. Information Processing in Agriculture, 7: 318-332.

Carmen Bas M., Ortiz J, Ballesteros L., and Martorell S. 2017. Forecasting7BE concentrations in surface air using time series analysis. Atmospheric Environment, 155: 154-161.

Choubin B., Moradi E., Golshan M., Adamowski J., Sajedi-Hosseini F., and Mosavi A. 2019. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Science of the Total Environment, 651: 2087-2096.

Coblinski J.A., Giasson E., Dematte J.A.M., Carnieletto A., Costa J.J.F., and Vašát R. 2020. Prediction of soil texture classes through different wavelength regions of reflectance spectroscopy at various soil depths. Catena, 189(104485): 1-12.

Dehghani Banian S., Ghorbani Dashtaki SH., Mohammadi J., Khodaverdilo H., and Khalil Moghadam B. 2012. Comparing the performance of multiple linear regression and tree to predict saturated hydraulic conductivity and the inverse of macroscopic capillary length. Iranian Water Research Journal, 5(9): 193-204. (In Persian)

Ding X., Zhao Zh., Yang Q., Chen L., Tian Q., Li X., and Meng F.R. 2020. Model prediction of depth-specific soil texture distributions with artificial neural network: A case study in Yunfu, a typical area of Udults Zone, South. Computers and Electronics in Agriculture China, 169(105217): 1-13.

Hasanzadeh A., Zakeryan S., Mansouri Daneshvar M.R. 2019. Preparing soil color map using Landsat satellite imagery. Geographical Sciences Journal, 15(31): 23-33.

Langhammer J., and Česák J., 2016. Applicability of a nu-support vector regression model for the completion of missing data in hydrological time series. Water, 8(12): 560-575.

Mattikalli N.M. 1997. Soil color modeling for the visible and Near-Infrared bands of landsat sensors using laboratory spectral measurements. Remote Sensing Environment, 59: 14-28.

Menxin W., and Houquan L. 2016. A modified vegetation water supply index (MVWSI) and its application in drought monitoring over Sichuan and Chongqing, China. Journal of Integrative Agriculture, 15(9): 2132–2141.

Ostovari Y., Asgari K., and Motaghian H.R. 2015. Assessment of tree and multiple linear regressions in estimation of cation exchange capacity. Journal of Water and Soil, 29(3): 683-694.

Parviz L. 2020. Performance evaluation of remote sensing data with machine learning technique to determine soil color. Polish Journal of Soil Science, LIII/1: 97-116.  

Sahwan W., Lucke B., Kappas M., and Baumler R. 2018. Assessing the spatial variability of soil surface colors in northern Jordan using satellite data from Landsat-8 and Sentinel-2. European Journal of Remote Sensing, 51(1): 850-862.

Shafri H.Z.M., and Ramle F.S.H. 2009. A comparison of support vector machine and decision tree classification using satellite data of Langkawi Island. Information Technology Journal, 8(1): 64-70.

Shahabfar A., Ghulam A., and Eitzinger J. 2012. Drought monitoring in Iran using the perpendicular drought indices. International Journal of Applied Earth Observation and Geoinformation, 18: 119–127.

Stiglitz R., Mikhailova E., Post CH., Schlautman M., Sharp J., Pargas R., Glover B., and Mooney J. 2017. Soil color sensor data collection using a GPS-enabled smartphone application. Geoderma, 296: 108–114.

Wu W., Li A.D., He X.H., Ma R., Liu H.B., and Lv J.K. 2018. A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China. Computers and Electronics in Agriculture, 144: 86-93.

Yang J., and Stenzel J. 2006. Short-term load forecasting with increment regression tree. Electric Power Systems Research, 76: 880–888.