Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles 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 paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-time soil profile clustering and classification during field surveys

Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles 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 paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-time soil profile clustering and classification during field surveys

Optimization Of Fuzzy Evapotranspiration Model Through Neural Training With Input–Output Examples

In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accurate estimation of daily ET comparable to the FAO Penman–Monteith equation, and showed the advantages of the fuzzy approach over other methods. The estimation accuracy of the fuzzy models, however, depended on the shape of the membership functions and the control rules built by trial–and–error methods. This paper shows how the trial and error drawback is eliminated with the application of a fuzzy–neural system, which combines the advantages of fuzzy logic (FL) and artificial neural networks (ANN). The strategy consisted of fusing the FL and ANN on a conceptual and structural basis. The neural component provided supervised learning capabilities for optimizing the membership functions and extracting fuzzy rules from a set of input–output examples selected to cover the data hyperspace of the sites evaluated. The model input parameters were solar irradiance, relative humidity, wind speed, and air temperature difference. The optimized model was applied to estimate reference ET using independent climatic data from the sites, and the estimates were compared with direct ET measurements from grass–covered lysimeters and estimations with the FAO Penman–Monteith equation. The model–estimated ET vs. lysimeter–measured ET gave a coefficient of determination (r2) value of 0.88 and a standard error of the estimate (Syx) of 0.48 mm d–1. For the same set of independent data, the FAO Penman–Monteith–estimated ET vs. lysimeter–measured ET gave an r2 value of 0.85 and an Syx value of 0.56 mm d–1. These results show that the optimized fuzzy–neural–model is reasonably accurate, and is comparable to the FAO Penman–Monteith equation. This approach can provide an easy and efficient means of tuning fuzzy ET models

Investigation Of A Fuzzy-Neural Network Application In Classification Of Soils Using Ground-Penetrating Radar Imagery

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

Soybean Yield, Evapotranspiration, Water Productivity, And Soil Water Extraction Response To Subsurface Drip Irrigation And Fertigation

Soybean [Glycine max (L.) Merr.] yield, irrigation water use efficiency (IWUE), crop water use efficiency (CWUE), evapotranspiration water use efficiency (ETWUE), and soil water extraction response to eleven treatments of full, limited, or delayed irrigation versus a rainfed control were investigated using a subsurface drip irrigation (SDI) system at a research site in south-central Nebraska. The SDI system laterals were 0.40 m deep in every other row middle of 0.76 m spaced plant rows. Actual evapotranspiration (ETa) was quantified in all treatments and used to schedule irrigation events on a 100% ETa replacement basis in all but three of the eleven treatments (i.e., 75% ETa replacement was used in two, and 60% ETa replacement was used in one). The irrigation amount (Ia) applied at each event was 100% of the ETa amount, except for two 100% ETa treatments in which only 65% or 50% of the water needed to cover the treatment plot area was applied to enable a test of a partial surface area-based irrigation approach. The first irrigation event was delayed until soybean stage R3 (begin pod) in two 100% Ia treatments, but thereafter they were irrigated with either 100% or 75% ETa replacement. Two 100% ETa and 100% Ia treatments also were used to evaluate soybean response to nitrogen (N) application methods (i.e., a preplant method versus N injection using the SDI system). Soybean ETa varied from 452 mm for the rainfed treatment to 600 mm (30% greater) for the fully irrigated treatment (100% ETa and 100% Ia) in 2007, and from 473 to 579 mm (20% greater) for the same treatments, respectively, in 2008. Among the irrigated treatments, 100% ETa and 65% Ia had the lowest 2007 ETa value (557 mm), whereas 100% ETa and 50% Ia had the lowest 2008 ETa (498 mm). The 100%, 75%, and 60% ETa treatments with 100% Ia had respective actual ETa values that declined linearly in 2008 (i.e., 579, 538, and 498 mm), but not in 2007. Seasonal totals for ETa versus Ia exhibited a linear relationship (R2 = 0.68 in 2007 and R2 = 0.67 in 2008). Irrigation enhanced soybean yields from rainfed yield baselines of 4.04 ton ha-1 in 2007 and 4.82 ton ha-1 in 2008) to a maximum of 4.94 ton ha-1 attained in 2007 with the delay to R3 irrigation treatment (its yield was significantly greater, p \u3c 0.05, than that of the seven other treatments) and 4.97 ton ha-1 attained in 2008 with the 100% ETa and 100% Ia preplant N treatment. Seed yield had a quadratic relationship with irrigation water applied and a linear relationship with ETa that was stronger in the drier year of 2007. Each 25.4 mm incremental increase in seasonal irrigation water applied increased soybean yield by 0.323 ton ha-1 (beyond the intercept) in 2007 and by 0.037 ton ha-1 in 2008. Each 25.4 mm increase in ETa generated a yield increase of 0.114 ton ha-1 (beyond the intercept) in 2007, but only 0.02 ton ha-1 in the wetter year of 2008. This research demonstrated that delaying the onset of irrigation until the R3 stage and practicing full irrigation thereafter for soybean grown on silt loam soils resulted in yields (and crop water productivity) that were similar to full-season irrigation scheduling strategies, and this result may be applicable in other regions with edaphic and climatic characteristics similar to those in south-central Nebraska

Soybean Yield, Evapotranspiration, Water Productivity, And Soil Water Extraction Response To Subsurface Drip Irrigation And Fertigation

Soybean [Glycine max (L.) Merr.] yield, irrigation water use efficiency (IWUE), crop water use efficiency (CWUE), evapotranspiration water use efficiency (ETWUE), and soil water extraction response to eleven treatments of full, limited, or delayed irrigation versus a rainfed control were investigated using a subsurface drip irrigation (SDI) system at a research site in south-central Nebraska. The SDI system laterals were 0.40 m deep in every other row middle of 0.76 m spaced plant rows. Actual evapotranspiration (ETa) was quantified in all treatments and used to schedule irrigation events on a 100% ETa replacement basis in all but three of the eleven treatments (i.e., 75% ETa replacement was used in two, and 60% ETa replacement was used in one). The irrigation amount (Ia) applied at each event was 100% of the ETa amount, except for two 100% ETa treatments in which only 65% or 50% of the water needed to cover the treatment plot area was applied to enable a test of a partial surface area-based irrigation approach. The first irrigation event was delayed until soybean stage R3 (begin pod) in two 100% Ia treatments, but thereafter they were irrigated with either 100% or 75% ETa replacement. Two 100% ETa and 100% Ia treatments also were used to evaluate soybean response to nitrogen (N) application methods (i.e., a preplant method versus N injection using the SDI system). Soybean ETa varied from 452 mm for the rainfed treatment to 600 mm (30% greater) for the fully irrigated treatment (100% ETa and 100% Ia) in 2007, and from 473 to 579 mm (20% greater) for the same treatments, respectively, in 2008. Among the irrigated treatments, 100% ETa and 65% Ia had the lowest 2007 ETa value (557 mm), whereas 100% ETa and 50% Ia had the lowest 2008 ETa (498 mm). The 100%, 75%, and 60% ETa treatments with 100% Ia had respective actual ETa values that declined linearly in 2008 (i.e., 579, 538, and 498 mm), but not in 2007. Seasonal totals for ETa versus Ia exhibited a linear relationship (R2 = 0.68 in 2007 and R2 = 0.67 in 2008). Irrigation enhanced soybean yields from rainfed yield baselines of 4.04 ton ha-1 in 2007 and 4.82 ton ha-1 in 2008) to a maximum of 4.94 ton ha-1 attained in 2007 with the delay to R3 irrigation treatment (its yield was significantly greater, p \u3c 0.05, than that of the seven other treatments) and 4.97 ton ha-1 attained in 2008 with the 100% ETa and 100% Ia preplant N treatment. Seed yield had a quadratic relationship with irrigation water applied and a linear relationship with ETa that was stronger in the drier year of 2007. Each 25.4 mm incremental increase in seasonal irrigation water applied increased soybean yield by 0.323 ton ha-1 (beyond the intercept) in 2007 and by 0.037 ton ha-1 in 2008. Each 25.4 mm increase in ETa generated a yield increase of 0.114 ton ha-1 (beyond the intercept) in 2007, but only 0.02 ton ha-1 in the wetter year of 2008. This research demonstrated that delaying the onset of irrigation until the R3 stage and practicing full irrigation thereafter for soybean grown on silt loam soils resulted in yields (and crop water productivity) that were similar to full-season irrigation scheduling strategies, and this result may be applicable in other regions with edaphic and climatic characteristics similar to those in south-central Nebraska