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The use of proximal soil sensor data fusion and digital soil mapping for precision agriculture
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Edité par CCSD -
Proximal soil sensing (PSS) is a promising approach when it comes to detailed characterizationof spatial soil heterogeneity. Since none of existing PSS systems can measure all soil informationneeded for implementation precision agriculture, sensor data fusion can provide a reasonable al-ternative to characterize the complexity of soils. In this study, we fused the data measured using agamma-ray sensor, an apparent electrical conductivity (ECa) sensor, and a commercial Veris MSP3platform including a optical sensor measuring soil reflectance at 660 nm and 940 nm, a soil ECasensor and a pH sensor, with the addition of topography for the prediction of several soil properties,i.e. soil organic matter, pH, buffer pH, phosphorus, potassium, calcium, magnesium, aluminum.A total of 65 sampling locations were selected from a 38.5 ha field in Ontario, Canada. Amongthem, 35 locations were selected by a random stratified sampling strategy. The stratification gridwas 1 ha. Sampling was prohibited in areas near the field boundaries and within a safety marginfrom the drainage system. 20 locations were selected using a neighbourhood search approach , aspatial data integration strategy. These two sample datasets were used as the calibration datasetto build the model between soil properties and readings from different proximal soil sensors. Theremaining 10 sensing locations were used as an independent validation dataset. Partial least squareregression (PLSR) was performed on the data from each individual sensor and different sensor com-binations (sensor data fusion). For most soil properties, predictions based on sensor data fusionwere better than those based on the output of individual sensors. By fusing the data from all of theproximal soil sensors, more properties can be predicted simultaneously (R2>0.5, and RPD>1.50).After choosing the optimal sensor combination for each soil property, different digital soil mappingmethods, including support vector machines (SVM), random forest (RF), multivariate adaptiveregression splines (MARS), regression trees (RT) and back-propagation artificial neural network(BP-ANN) were used to estimate variograms and pursue regression kriging. High resolution mapswere thus interpolated with the most successful methods. The performance of the two differentsampling strategies was compared by the prediction accuracy from the validation samples. Wethus conclude that proximal soil sensor fusion paired with the digital soil mapping method is apromising way to offer the essential soil information needed for precision agriculture.
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