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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>
For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Article 2021 Italy EnglishPublisher:Multidisciplinary Digital Publishing Institute Funded by:EC | TAILOREC| TAILORDario Albani; Wolfgang Hönig; Daniele Nardi; Nora Ayanian; Vito Trianni;doi: 10.3390/app11073115
handle: 11573/1620438
Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.
Applied Sciences; Ar... arrow_drop_down Applied Sciences; Archivio della ricerca- Università di Roma La Sapienza; OpenAIREOther literature type . Article . Other ORP type . 2021 . Peer-reviewedLicense: CC BYArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzaadd 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/app11073115&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Applied Sciences; Ar... arrow_drop_down Applied Sciences; Archivio della ricerca- Università di Roma La Sapienza; OpenAIREOther literature type . Article . Other ORP type . 2021 . Peer-reviewedLicense: CC BYArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzaadd 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/app11073115&type=result"></script>'); --> </script>
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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>
For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Article 2021 Italy EnglishPublisher:Multidisciplinary Digital Publishing Institute Funded by:EC | TAILOREC| TAILORDario Albani; Wolfgang Hönig; Daniele Nardi; Nora Ayanian; Vito Trianni;doi: 10.3390/app11073115
handle: 11573/1620438
Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.
Applied Sciences; Ar... arrow_drop_down Applied Sciences; Archivio della ricerca- Università di Roma La Sapienza; OpenAIREOther literature type . Article . Other ORP type . 2021 . Peer-reviewedLicense: CC BYArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzaadd 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/app11073115&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Applied Sciences; Ar... arrow_drop_down Applied Sciences; Archivio della ricerca- Università di Roma La Sapienza; OpenAIREOther literature type . Article . Other ORP type . 2021 . Peer-reviewedLicense: CC BYArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzaadd 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/app11073115&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu