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apps Other research productkeyboard_double_arrow_right Article 2022 Portugal, NetherlandsPublisher:MDPI AG Funded by:UKRI | EV Fleet-Centred Local En..., EC | SOILdarityUKRI| EV Fleet-Centred Local Energy System ,EC| SOILdarityAuthors: Lalit M. Kandpal; Muhammad A. Munnaf; Cristina Cruz; Abdul M. Mouazen;Lalit M. Kandpal; Muhammad A. Munnaf; Cristina Cruz; Abdul M. Mouazen;Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R2p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R2p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R2p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R2p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications.
Research@WUR; Sensor... arrow_drop_down Research@WUR; Sensors; OpenAIREOther literature type . Article . Other ORP type . 2022 . Peer-reviewedLicense: CC BYFull-Text: https://www.mdpi.com/1424-8220/22/9/3459/pdfUniversidade de Lisboa: Repositório.ULArticle . 2022License: CC BYData sources: Universidade de Lisboa: Repositório.ULadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 11 citations 11 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 10visibility views 10 download downloads 1 Powered bymore_vert Research@WUR; Sensor... arrow_drop_down Research@WUR; Sensors; OpenAIREOther literature type . Article . Other ORP type . 2022 . Peer-reviewedLicense: CC BYFull-Text: https://www.mdpi.com/1424-8220/22/9/3459/pdfUniversidade de Lisboa: Repositório.ULArticle . 2022License: CC BYData sources: Universidade de Lisboa: Repositório.ULadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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apps Other research productkeyboard_double_arrow_right Article 2022 Portugal, NetherlandsPublisher:MDPI AG Funded by:UKRI | EV Fleet-Centred Local En..., EC | SOILdarityUKRI| EV Fleet-Centred Local Energy System ,EC| SOILdarityAuthors: Lalit M. Kandpal; Muhammad A. Munnaf; Cristina Cruz; Abdul M. Mouazen;Lalit M. Kandpal; Muhammad A. Munnaf; Cristina Cruz; Abdul M. Mouazen;Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R2p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R2p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R2p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R2p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications.
Research@WUR; Sensor... arrow_drop_down Research@WUR; Sensors; OpenAIREOther literature type . Article . Other ORP type . 2022 . Peer-reviewedLicense: CC BYFull-Text: https://www.mdpi.com/1424-8220/22/9/3459/pdfUniversidade de Lisboa: Repositório.ULArticle . 2022License: CC BYData sources: Universidade de Lisboa: Repositório.ULadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/s22093459&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 11 citations 11 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 10visibility views 10 download downloads 1 Powered bymore_vert Research@WUR; Sensor... arrow_drop_down Research@WUR; Sensors; OpenAIREOther literature type . Article . Other ORP type . 2022 . Peer-reviewedLicense: CC BYFull-Text: https://www.mdpi.com/1424-8220/22/9/3459/pdfUniversidade de Lisboa: Repositório.ULArticle . 2022License: CC BYData sources: Universidade de Lisboa: Repositório.ULadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/s22093459&type=result"></script>'); --> </script>
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