Cargando…
3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data
The integration of local agricultural knowledge deepens the understanding of complex phenomena such as the association between climate variability, crop yields and undernutrition. Participatory Sensing (PS) is a concept which enables laymen to easily gather geodata with standard low-cost mobile devi...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830550/ https://www.ncbi.nlm.nih.gov/pubmed/27073917 http://dx.doi.org/10.1371/journal.pone.0152839 |
_version_ | 1782426912399818752 |
---|---|
author | Marx, Sabrina Hämmerle, Martin Klonner, Carolin Höfle, Bernhard |
author_facet | Marx, Sabrina Hämmerle, Martin Klonner, Carolin Höfle, Bernhard |
author_sort | Marx, Sabrina |
collection | PubMed |
description | The integration of local agricultural knowledge deepens the understanding of complex phenomena such as the association between climate variability, crop yields and undernutrition. Participatory Sensing (PS) is a concept which enables laymen to easily gather geodata with standard low-cost mobile devices, offering new and efficient opportunities for agricultural monitoring. This study presents a methodological approach for crop height assessment based on PS. In-field crop height variations of a maize field in Heidelberg, Germany, are gathered with smartphones and handheld GPS devices by 19 participants. The comparison of crop height values measured by the participants to reference data based on terrestrial laser scanning (TLS) results in R(2) = 0.63 for the handheld GPS devices and R(2) = 0.24 for the smartphone-based approach. RMSE for the comparison between crop height models (CHM) derived from PS and TLS data is 10.45 cm (GPS devices) and 14.69 cm (smartphones). Furthermore, the results indicate that incorporating participants’ cognitive abilities in the data collection process potentially improves the quality data captured with the PS approach. The proposed PS methods serve as a fundament to collect agricultural parameters on field-level by incorporating local people. Combined with other methods such as remote sensing, PS opens new perspectives to support agricultural development. |
format | Online Article Text |
id | pubmed-4830550 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-48305502016-04-22 3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data Marx, Sabrina Hämmerle, Martin Klonner, Carolin Höfle, Bernhard PLoS One Research Article The integration of local agricultural knowledge deepens the understanding of complex phenomena such as the association between climate variability, crop yields and undernutrition. Participatory Sensing (PS) is a concept which enables laymen to easily gather geodata with standard low-cost mobile devices, offering new and efficient opportunities for agricultural monitoring. This study presents a methodological approach for crop height assessment based on PS. In-field crop height variations of a maize field in Heidelberg, Germany, are gathered with smartphones and handheld GPS devices by 19 participants. The comparison of crop height values measured by the participants to reference data based on terrestrial laser scanning (TLS) results in R(2) = 0.63 for the handheld GPS devices and R(2) = 0.24 for the smartphone-based approach. RMSE for the comparison between crop height models (CHM) derived from PS and TLS data is 10.45 cm (GPS devices) and 14.69 cm (smartphones). Furthermore, the results indicate that incorporating participants’ cognitive abilities in the data collection process potentially improves the quality data captured with the PS approach. The proposed PS methods serve as a fundament to collect agricultural parameters on field-level by incorporating local people. Combined with other methods such as remote sensing, PS opens new perspectives to support agricultural development. Public Library of Science 2016-04-13 /pmc/articles/PMC4830550/ /pubmed/27073917 http://dx.doi.org/10.1371/journal.pone.0152839 Text en © 2016 Marx et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Marx, Sabrina Hämmerle, Martin Klonner, Carolin Höfle, Bernhard 3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data |
title | 3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data |
title_full | 3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data |
title_fullStr | 3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data |
title_full_unstemmed | 3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data |
title_short | 3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data |
title_sort | 3d participatory sensing with low-cost mobile devices for crop height assessment – a comparison with terrestrial laser scanning data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830550/ https://www.ncbi.nlm.nih.gov/pubmed/27073917 http://dx.doi.org/10.1371/journal.pone.0152839 |
work_keys_str_mv | AT marxsabrina 3dparticipatorysensingwithlowcostmobiledevicesforcropheightassessmentacomparisonwithterrestriallaserscanningdata AT hammerlemartin 3dparticipatorysensingwithlowcostmobiledevicesforcropheightassessmentacomparisonwithterrestriallaserscanningdata AT klonnercarolin 3dparticipatorysensingwithlowcostmobiledevicesforcropheightassessmentacomparisonwithterrestriallaserscanningdata AT hoflebernhard 3dparticipatorysensingwithlowcostmobiledevicesforcropheightassessmentacomparisonwithterrestriallaserscanningdata |