FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912648180> ?p ?o ?g. }

• W2912648180 endingPage "128" @default.
• W2912648180 startingPage "111" @default.
• W2912648180 abstract "In this research, proximal soil sensor data fusion was defined as a multifaceted process which integrates geospatially correlated data, or information, from multiple proximal soil sensors to accurately characterize the spatial complexity of soils. This has capability of providing improved understanding of soil heterogeneity for potential applications associated with crop production and natural resource management. To assess the potential of data fusion for the purpose of improving thematic soil mapping over the single sensor approach, data from multiple proximal soil sensors were combined to develop and validate predictive relationships with laboratory-measured soil physical and chemical properties. The work was conducted in an agricultural field with both mineral and organic soils. The integrated data included: topography records obtained using a real-time kinetic (RTK) global navigation satellite system (GNSS) receiver, apparent soil electrical conductivity (ECa) obtained using an electromagnetic induction sensor, and content of several naturally occurring radioisotopes detected using a mobile gamma-ray spectrometer. In addition, the soil profile data were collected using a commercial ruggedized multi-sensor platform carrying a visible and near-infrared (vis-NIR) optical sensor and a galvanic contact soil ECa sensor. The measurements were carried out at predefined field locations covering the entire study area identified from sensor measured a priori information on field elevation, ECa and gamma-ray count. The information was used to predict: soil organic matter (SOM), pH, lime buffer capacity (LBC), as well as concentration of phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and aluminum (Al). Partial least squares regressions (PLSRs) were used to predict soil properties from individual sensors and different sensor combinations (sensor data fusion). By integrating the data from all of the proximal soil sensors, SOM, pH, LBC, Ca, Mg, and Al were predicted simultaneously with R2 > 0.5 (RPD > 1.50). Improved predictions were observed for most soil properties based on sensor data fusion than those based on individual sensors. After choosing the optimal sensor combination for each soil property, the predictive capability was compared using different data mining algorithms, including support vector machines (SVM), random forest (RF), multivariate adaptive regression splines (MARS), and regression trees (CART). Improved predictions for SOM, Ca, Mg, and Al were observed using SVM over PLSR. The predictive capability was followed by RF and MARS, with CART. Predictions of pH and LBC were only feasible using MARS and PLSR, respectively. In this field, it was not possible to predict extractable P and K using all tested sensor combinations or algorithms. With large variability in SOM, the field presents a special situation and thus, the result could be specific to the study site. Further research includes an extended number of experimental sites covering different geographic areas around Eastern Canada." @default.
• W2912648180 created "2019-02-21" @default.
• W2912648180 creator A5010230084 @default.
• W2912648180 creator A5016449371 @default.
• W2912648180 creator A5025531957 @default.
• W2912648180 creator A5026025391 @default.
• W2912648180 creator A5056867421 @default.
• W2912648180 creator A5077536887 @default.
• W2912648180 creator A5080056117 @default.
• W2912648180 creator A5087929948 @default.
• W2912648180 date "2019-05-01" @default.
• W2912648180 modified "2023-10-18" @default.
• W2912648180 title "Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study" @default.
• W2912648180 cites W1934353085 @default.
• W2912648180 cites W1969214686 @default.
• W2912648180 cites W1973273412 @default.
• W2912648180 cites W1975135213 @default.
• W2912648180 cites W1975714136 @default.
• W2912648180 cites W1979472406 @default.
• W2912648180 cites W1990344734 @default.
• W2912648180 cites W1998053851 @default.
• W2912648180 cites W2025015460 @default.
• W2912648180 cites W2027368520 @default.
• W2912648180 cites W2028992283 @default.
• W2912648180 cites W2030925077 @default.
• W2912648180 cites W2035424054 @default.
• W2912648180 cites W2038420319 @default.
• W2912648180 cites W2041859801 @default.
• W2912648180 cites W2047763198 @default.
• W2912648180 cites W2051670289 @default.
• W2912648180 cites W2053185220 @default.
• W2912648180 cites W2058907420 @default.
• W2912648180 cites W2064334041 @default.
• W2912648180 cites W2065970148 @default.
• W2912648180 cites W2066194358 @default.
• W2912648180 cites W2066384929 @default.
• W2912648180 cites W2066633707 @default.
• W2912648180 cites W2075512496 @default.
• W2912648180 cites W2084341220 @default.
• W2912648180 cites W2084865201 @default.
• W2912648180 cites W2090507729 @default.
• W2912648180 cites W2097862545 @default.
• W2912648180 cites W2102201073 @default.
• W2912648180 cites W2110832913 @default.
• W2912648180 cites W2133297572 @default.
• W2912648180 cites W2142624305 @default.
• W2912648180 cites W2143399945 @default.
• W2912648180 cites W2144280800 @default.
• W2912648180 cites W2145488820 @default.
• W2912648180 cites W2158449659 @default.
• W2912648180 cites W2163496225 @default.
• W2912648180 cites W2200121095 @default.
• W2912648180 cites W2237120293 @default.
• W2912648180 cites W2280367255 @default.
• W2912648180 cites W2326266029 @default.
• W2912648180 cites W2499991491 @default.
• W2912648180 cites W2563958088 @default.
• W2912648180 cites W2590756268 @default.
• W2912648180 cites W2593356872 @default.
• W2912648180 cites W2611476520 @default.
• W2912648180 cites W2620093133 @default.
• W2912648180 cites W2620736583 @default.
• W2912648180 cites W2623853268 @default.
• W2912648180 cites W2730811962 @default.
• W2912648180 cites W2735877794 @default.
• W2912648180 cites W2766300505 @default.
• W2912648180 cites W2911964244 @default.
• W2912648180 cites W40397213 @default.
• W2912648180 cites W2529564319 @default.
• W2912648180 doi "https://doi.org/10.1016/j.geoderma.2019.01.006" @default.
• W2912648180 hasPublicationYear "2019" @default.
• W2912648180 type Work @default.
• W2912648180 sameAs 2912648180 @default.
• W2912648180 citedByCount "67" @default.
• W2912648180 countsByYear W29126481802019 @default.
• W2912648180 countsByYear W29126481802020 @default.
• W2912648180 countsByYear W29126481802021 @default.
• W2912648180 countsByYear W29126481802022 @default.
• W2912648180 countsByYear W29126481802023 @default.
• W2912648180 crossrefType "journal-article" @default.
• W2912648180 hasAuthorship W2912648180A5010230084 @default.
• W2912648180 hasAuthorship W2912648180A5016449371 @default.
• W2912648180 hasAuthorship W2912648180A5025531957 @default.
• W2912648180 hasAuthorship W2912648180A5026025391 @default.
• W2912648180 hasAuthorship W2912648180A5056867421 @default.
• W2912648180 hasAuthorship W2912648180A5077536887 @default.
• W2912648180 hasAuthorship W2912648180A5080056117 @default.
• W2912648180 hasAuthorship W2912648180A5087929948 @default.
• W2912648180 hasBestOaLocation W29126481803 @default.
• W2912648180 hasConcept C119857082 @default.
• W2912648180 hasConcept C127313418 @default.
• W2912648180 hasConcept C159390177 @default.
• W2912648180 hasConcept C159750122 @default.
• W2912648180 hasConcept C182124840 @default.
• W2912648180 hasConcept C33954974 @default.
• W2912648180 hasConcept C39432304 @default.
• W2912648180 hasConcept C41008148 @default.
• W2912648180 hasConcept C50516716 @default.

• next