Assessing lodging severity over an experimental maize (Zea Mays L.) Field using UAS images

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dc.contributor.authorChu, Tianxing
dc.contributor.authorStarek, Michael J.
dc.contributor.authorBrewer, Michael J.
dc.contributor.authorMurray, Seth C.
dc.contributor.authorPruter, Luke S.
dc.creator.orcidhttps://orcid.org/0000-0002-7996-0594en_US
dc.creator.orcidhttps://orcid.org/0000-0002-7996-0594
dc.creator.orcidhttps://orcid.org/0000-0002-7996-0594https://orcid.org/0000-0002-7996-0594
dc.creator.orcidhttps://orcid.org/0000-0002-7996-0594
dc.creator.orcidhttps://orcid.org/0000-0002-7996-0594
dc.date.accessioned2021-10-28T19:13:57Z
dc.date.available2021-10-28T19:13:57Z
dc.date.issued2017-09-04
dc.description.abstractLodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms, this study investigated the potential of high resolution imaging with unmanned aircraft system (UAS) technology for detecting and assessing lodging severity over an experimental maize field at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, during the 2016 growing season. The method was proposed to not only detect the occurrence of lodging at the field scale, but also to quantitatively estimate the number of lodged plants and the lodging rate within individual rows. Nadir-view images of the field trial were taken by multiple UAS platforms equipped with consumer grade red, green, and blue (RGB), and near-infrared (NIR) cameras on a routine basis, enabling a timely observation of the plant growth until harvesting. Models of canopy structure were reconstructed via an SfM photogrammetric workflow. The UAS-estimated maize height was characterized by polygons developed and expanded from individual row centerlines, and produced reliable accuracy when compared against field measures of height obtained from multiple dates. The proposed method then segmented the individual maize rows into multiple grid cells and determined the lodging severity based on the height percentiles against preset thresholds within individual grid cells. From the analysis derived from this method, the UAS-based lodging results were generally comparable in accuracy to those measured by a human data collector on the ground, measuring the number of lodging plants (R2 = 0.48) and the lodging rate (R2 = 0.50) on a per-row basis. The results also displayed a negative relationship of ground-measured yield with UAS-estimated and ground-measured lodging rate.en_US
dc.description.abstractLodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms, this study investigated the potential of high resolution imaging with unmanned aircraft system (UAS) technology for detecting and assessing lodging severity over an experimental maize field at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, during the 2016 growing season. The method was proposed to not only detect the occurrence of lodging at the field scale, but also to quantitatively estimate the number of lodged plants and the lodging rate within individual rows. Nadir-view images of the field trial were taken by multiple UAS platforms equipped with consumer grade red, green, and blue (RGB), and near-infrared (NIR) cameras on a routine basis, enabling a timely observation of the plant growth until harvesting. Models of canopy structure were reconstructed via an SfM photogrammetric workflow. The UAS-estimated maize height was characterized by polygons developed and expanded from individual row centerlines, and produced reliable accuracy when compared against field measures of height obtained from multiple dates. The proposed method then segmented the individual maize rows into multiple grid cells and determined the lodging severity based on the height percentiles against preset thresholds within individual grid cells. From the analysis derived from this method, the UAS-based lodging results were generally comparable in accuracy to those measured by a human data collector on the ground, measuring the number of lodging plants (R2 = 0.48) and the lodging rate (R2 = 0.50) on a per-row basis. The results also displayed a negative relationship of ground-measured yield with UAS-estimated and ground-measured lodging rate.
dc.identifier.citationChu, T., Starek, M.J., Brewer, M.J., Murray, S.C. and Pruter, L.S., 2017. Assessing lodging severity over an experimental maize (Zea mays L.) field using UAS images. Remote Sensing, 9(9), p.923.en_US
dc.identifier.citationChu, T., Starek, M.J., Brewer, M.J., Murray, S.C. and Pruter, L.S., 2017. Assessing lodging severity over an experimental maize (Zea mays L.) field using UAS images. Remote Sensing, 9(9), p.923.
dc.identifier.doihttps://doi.org/10.3390/rs9090923
dc.identifier.urihttps://hdl.handle.net/1969.6/89916
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherMDPIen_US
dc.publisherMDPI
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectunmanned aircraft systemsen_US
dc.subjectmaize lodgingen_US
dc.subjectstructure from motion photogrammetryen_US
dc.subjectcrop heighten_US
dc.subjectcrop height modellingen_US
dc.subjectmultivariate regressionen_US
dc.subjectlodging rateen_US
dc.subjectunmanned aircraft systems
dc.subjectmaize lodging
dc.subjectstructure from motion photogrammetry
dc.subjectcrop height
dc.subjectcrop height modelling
dc.subjectmultivariate regression
dc.subjectlodging rate
dc.titleAssessing lodging severity over an experimental maize (Zea Mays L.) Field using UAS imagesen_US
dc.titleAssessing lodging severity over an experimental maize (Zea Mays L.) Field using UAS images
dc.typeArticleen_US
dc.typeArticle

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