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dc.contributor.advisorDotray, Peter A
dc.creatorTorres, U
dc.date.accessioned2021-10-13T19:52:50Z
dc.date.available2021-10-13T19:52:50Z
dc.date.created2021-08
dc.date.issued2021-07-09
dc.date.submittedAugust 2021
dc.identifier.urihttps://hdl.handle.net/2346/88095
dc.description.abstractAuxinic herbicides like 2,4-D allow cotton growers to use an additional mode of action to control troublesome weeds including glyphosate-resistant Palmer amaranth (Amaranthus palmeri S. Wats). 2,4-D is effective at controlling small broadleaf weeds when applied postemergence. However, off-target movement to susceptible cotton varieties is a major concern. Non-tolerant cotton is highly sensitive to 2,4-D and will exhibit symptomology at very low rates. With unmanned aerial systems (UAS)-based data collection, there is the potential to identify vegetation indices (VIs) that are effective at detecting herbicide induced crop injury. Research was conducted at the Texas Tech New Deal Research Farm near Lubbock, TX to detect and evaluate cotton response to low rates of 2,4-D using UAS-based multispectral data. Treatments included a non-treated control, five low rates of 2,4-D (1.07 g ae ha-1, 2.14 g ae ha-1, 10.7 g ae ha-1, 21.4 g ae ha-1, 107 g ae ha-1), and the full labeled rate of 2,4-D (1,070 g ae ha-1). Herbicides were applied using a CO2-pressurized backpack sprayer with a carrier volume of 140 liters per hectare to DP 1822XF cotton at first square plus two weeks. Flights at an altitude of 30 meters were conducted at 0, 6, 15, 21, 28, 35, 43, and 51 days after the herbicide applications. Several vegetation indices were applied to the multispectral data and differences between treatments were assessed with and without the soil background at three spatial resolutions (1.3 cm pixel-1, 5.2 cm pixel-1, and 10.4 cm pixel-1). Visible injury was observed on all 2,4-D-exposed cotton 9 days after application. The most severe cotton injury occurred with the 1X rate of 2,4-D at all visual rating dates and was the most detectable with VIs starting at 6 days after application (DAA). The GSAVI, MNLI, MSAVI2, RDVI, and SAVI were effective at distinguishing between the untreated, the five low 2,4-D rates, and the 1X rate at 15 DAA with or without the soil background at all spatial resolutions. At 21, 28, and 35 DAA, all VIs were able to differentiate between the 1X rate and the sub-labelled rates except for the ARI2, GCI, and TCARI at 21 DAA and TCARI at 28 DAA. Better results were observed using the VIs when the soil background was included at 43 and 51 DAA. Injury increased in the treated plots as days after treatment increased, which resulted in a greater number of VIs showing differences in values between treatments. The soil adjusted VIs were more successful at detecting differences between treatments than the non-soil adjusted VIs. Decreasing the spatial resolution did not improve the results.
dc.format.mimetypeapplication/pdf
dc.subjectHerbicide injury
dc.subjectvegetation indices
dc.subjectunmanned aerial systems
dc.titleHerbicide Injury Detection in Cotton Using Unmanned Aerial System-based Multispectral Imagery
dc.typeThesis
dc.date.updated2021-10-13T19:52:51Z
dc.type.materialtext
thesis.degree.nameMaster of Science
thesis.degree.levelMasters
thesis.degree.disciplinePlant and Soil Science
thesis.degree.grantorTexas Tech University
thesis.degree.departmentPlant and Soil Science
dc.contributor.committeeMemberGuo, Wenxuan
dc.contributor.committeeMemberMaeda, Murilo M
dc.creator.orcid0000-0002-5718-2816


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