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Published byElwin Erik Spencer Modified over 6 years ago
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Clay content prediction using on-the-go proximal soil sensor fusion
Salman Tabatabai, Maria Knadel Mogens H. Greve Department of Agroecology Aarhus University Denmark
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Outline Background Materials and Methods Results Conclusions
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Background Materials and Methods Results Conclusions
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The problem High demand for detailed texture maps
Detailed maps require large amount of samples Collecting this amount of samples is expensive and time-consuming; so is their analysis Water holding capacity, solute transport, hydraulic conductivity, evaporation, root growth, fertility, and much more is to a large extent influenced by texture parameters.
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Why on-the-go Sensors? Inexpensive Rapid Highly reproducible
Environmental friendly Applicable in large scale
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Why Clay? Clay maps: Major reference for fertilizer and pesticide application authorization in Denmark Have the figures for reproducibility of other methods,
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Background Materials and Methods Results Conclusions
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Study Sites Silstrup Havris 1-3 Foulum Voulund 1-2 Estrup
Field Name Area (Ha) No of data points Total Cal samples Soil Type (USDA) Havris 1 1.5 1928 15 sand Havris 2 1760 Havris 3 1.9 2061 Silstrup 1.7 2718 20 sandy loam Estrup 1.2 1801 loamy sand Foulum 8 4683 Voulund 1 13 7490 Voulund 2 11 5146 Estrup Map from Google® as retreived in QGIS®
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On-the-go Soil Sensor Veris® Mobile Sensor Platform:
Vis-NIR Spectrometer Electrical Conductivity Temperature (Veris Technologies, Salina, KS) nm 384 bands In this slide briefly introduce the sensor EC_Sh (0-30 cm) EC_Dp (0-90 cm)
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Image from Google® as retreived in QGIS®
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Spectra+EC+T+Pretreatments
Locations PC Clustering (Spectra) On-the-go Sensor Model Input Calibration samples Hydrometry (Clay) R2=0.93 RMSE=1.06 RPIQ=5.2 CV Random (20 segments) R2=0.75 RMSE=2.17 RPIQ=2.5 PLS Calibration CV One-Field-Out (8 segments) R2=0.93 RMSE=0.96 RPIQ=5.7 Test Set KStone We have on-the-go sensor towed by a tractor. Model Input R2=0.94 RMSE=0.91 RPIQ=6.0 SVM Calibration CV Random (20 segments)
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Locations Spectra Only Extensive Noise Removal PC Clustering (Spectra) On-the-go Sensor Model Input Calibration samples Hydrometry (Clay) R2=0.94 RMSE=0.99 RPIQ=5.6 CV Random (20 segments) R2=0.74 RMSE=2.25 RPIQ=2.4 PLS Calibration CV One-Field-Out (8 segments) R2=0.95 RMSE=0.79 RPIQ=7.0 Test Set KStone We have on-the-go sensor towed by a tractor. Model Input R2=0.94 RMSE=0.95 RPIQ=5.8 SVM Calibration CV Random (20 segments)
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Background Materials and Methods Results Conclusions
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Although the two modeling approaches overall similar results, RMSEV in the spectra only models are lower than the fused sensor models and the R2 are higher. In the case of Kstoen Test Set, the cleaned spectra RMSEV is about 18 percent lower compared to the fused sensors. In other cases, there are negligable differences in R2 and RMSEV between the two model ing approaches. RPIQ=7.0 4 PLS Factors
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Background Materials and Methods Results Conclusions
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Conclusions One-the-go sensors can predict clay content accurately and robustly even in fields with high variability of clay content and in different conditions In cases that no pretreatments on spectra is possible, fusing spectral data with EC and T may improve the predictions Meticulous and careful preprocessing and noise removal of spectral data can provide the best predictions without the need for fusing it with any auxiliary data
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Aarhus University, Denmark
Conclusions (cont’d) It is imperative to note that the findings of this research might be only applicable in areas with sand as major soil particle size (e.g. in Denmark) More research on soils with higher clay and silt content is required before a conclusive decision is made on designing new mobile sensor platforms. Additive note: There is a need for further development of on-the-go sensor spectral library in order to enhance the model usability in highly different fields. Salman Tabatabai Aarhus University, Denmark
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