01597naa a2200253 a 450000100080000000500110000800800410001902200140006002400540007410000240012824501010015226000090025352008290026265000340109165000190112565000190114465000090116365000170117265000200118970000200120970000180122970000230124777300730127021227352020-05-28       2020    bl uuuu     u00u1 u    #d  a0168-16997 ahttps://doi.org/10.1016/j.compag.2020.1054352DOI1 aMORAIS, P. A. de O.  aA computer-assisted soil texture analysis using digitally scanned images.h[electronic resource]  c2020  aA computer-assisted soil texture analysis is presented using digitally scanned soil images of 177 soil samples collected from different regions of Brazil. Soil digital images were correlated to texture results determined by the standard pipette method using three multivariate methods: successive projections algorithm combined with multivariate linear regression (SPA-MLR), partial least-squares regression (PLSR), and least-squares support vector machine regression (LSSVMR). Sand and clay particle size were better estimated using LSSVMR presenting correlations above 90%. Following soil sample particle size content estimates, soil texture classes were also estimated achieving 90.6% accuracy. The proposed method using digital images is fast, cheap and has low environmental impact when compared to the standard method.  aComputer-assisted instruction  aDigital images  aImage analysis  aSand  aSoil texture  aTextura do Solo1 aSOUZA, D. M. de1 aMADARI, B. E.1 aOLIVEIRA, A. E. de  tComputers and Electronics in Agriculturegv. 174, 105435, July 2020.