Biological Systems Engineering


Date of this Version



Applied Engineering in Agriculture Vol. 20(1): 109-117


Copyright 2004 American Society of Agricultural Engineers


Errors associated with visual inspection and interpretations of radargrams often inhibit the intensive surveying

of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this article presents an

application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profiles using

GPR imagery. The classifier clusters and classifies soil profile strips along a traverse based on common pattern similarities

that can relate to physical features of the soil (e.g., number of horizons; depth, texture, and structure of the horizons; and

relative arrangement of the horizons, etc.). This article illustrates this classification procedure by its application on GPR data,

both simulated and actual. Results show that the procedure is able to classify the profile into zones that corresponded with

the classifications obtained by visual inspection and interpretation of radar grams. Application of F-NN to a study site in

southwest Tennessee gave soil groupings that are in close correspondence with the groupings obtained in a previous study,

which used the traditional methods of complete soil morphological, chemical, and physical characterization. At a crossover

value of 3.0, the F-NN soil grouping boundary locations fall within a range of ±2.7 m from the soil groupings determined

by the traditional methods. These results indicate that F-NN can supply accurate real-time soil profile clustering and

classification during field surveys.