Dataset Information

3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment--A Comparison with Terrestrial Laser Scanning Data.

ABSTRACT: 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 R2 = 0.63 for the handheld GPS devices and R2 = 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.

SUBMITTER: Marx S

PROVIDER: S-EPMC4830550 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment--A Comparison with Terrestrial Laser Scanning Data.

Marx Sabrina S Hämmerle Martin M Klonner Carolin C Höfle Bernhard B

PloS one 20160413 4

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 ma ...[more]

PMID: 27073917

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Project description:BACKGROUND:A large provider of community health services (an NHS Trust in England) deployed Apple iPads to its front-line community-based healthcare clinicians (predominantly nurses) to enable them to increase responsiveness to patients' and their families' needs. We conducted a participatory formative evaluation of this iPad initiative among different users and the informatics teams implementing it, to establish how such initiatives can sustain adoption and achieve their stated benefits. METHODS:We used a participatory approach involving a partnership between study investigators and key decision-makers of the initiative to engage stakeholders in the study. Methods included focus groups and group discussion, meetings with key personnel and analysis of documents related to the initiative. Using a participatory technique, members of the organisation identified practical challenges to inform the on-going process of implementation and adoption in the Trust. RESULTS:Healthcare professionals identified many benefits associated with having iPads to support care delivery, including streamlined workflows and accessible information at the point-of-care in the community. However, challenges that interfered with implementation were also reported by both the team implementing the initiative (IT team) and early users. Challenges reported by IT team are: adopter clinicians' scepticism and suspicion; clinician non-compliance with training and operational guidance procedures; and managing adopter expectations. Challenges reported by users are: setting-up and maintaining the devices on a long-term basis; blurring of personal and professional boundaries; and disconnection from the IT team. Results show that these challenges could be overcome if there were more informal 'socialised' interactions between adopters and between adopters and the IT team. CONCLUSIONS:We suggest that similar initiatives require increased ongoing dialogue between different levels of stakeholder groups, in the form of socialised engagements, to avoid common misunderstandings and to promote the processes involved in co-constructing the initiative on a generally-agreed and sustainable basis.

Project description:This study aimed at illustrating how direct measurements, mobile laser scanning and hydraulic modelling can be combined to quantify environmental drivers, improve vegetation models and increase our understanding of vegetation patterns in a sub-arctic river valley. Our results indicate that the resultant vegetation models successfully predict riparian vegetation patterns (Rho = 0.8 for total species richness, AUC = 0.97 for distribution) and highlight differences between eight functional species groups (Rho 0.46-0.84; AUC 0.79-0.93; functional group-specific effects). In our study setting, replacing the laser scanning-based and hydraulic modelling-based variables with a proxy variable elevation did not significantly weaken the models. However, using directly measured and modelled variables allows relating species patterns to e.g. stream power or the length of the flood-free period. Substituting these biologically relevant variables with proxies mask important processes and may reduce the transferability of the results into other sites. At the local scale, the amount of litter is a highly important driver of total species richness, distribution and abundance patterns (relative influences 49, 72 and 83%, respectively) and across all functional groups (13-57%; excluding lichen species richness) in the sub-arctic river valley. Moreover, soil organic matter and soil water content shape vegetation patterns (on average 16 and 7%, respectively). Fluvial disturbance is a key limiting factor only for lichen, bryophyte and dwarf shrub species in this environment (on average 37, 6 and 10%, respectively). Fluvial disturbance intensity is the most important component of disturbance for most functional groups while the length of the disturbance-free period is more relevant for lichens. We conclude that striving for as accurate quantifications of environmental drivers as possible may reveal important processes and functional group differences and help anticipate future changes in vegetation. Mobile laser scanning, high-resolution digital elevation models and hydraulic modelling offer useful methodology for improving correlative vegetation models.

Dataset Information

3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment--A Comparison with Terrestrial Laser Scanning Data.

Publications

3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment--A Comparison with Terrestrial Laser Scanning Data.

Similar Datasets

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