Towards Autonomous Cotton Yield Monitoring

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dc.contributor.authorBrand, Howard James Jarrellen
dc.contributor.committeechairFurukawa, Tomonarien
dc.contributor.committeememberSandu, Corinaen
dc.contributor.committeememberThomson, Steven J.en
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2016-09-09T08:00:19Zen
dc.date.available2016-09-09T08:00:19Zen
dc.date.issued2016-09-08en
dc.description.abstractOne important parameter of interest in remote sensing to date is yield variability. Proper understanding of yield variability provides insight on the geo-positional dependences of field yields and insight on zone management strategies. Estimating cotton yield and observing cotton yield variability has proven to be a challenging problem due to the complex fruiting behavior of cotton from reactions to environmental conditions. Current methods require expensive sensory equipment on large manned aircrafts and satellites. Other systems, such as cotton yield monitors, are often subject to error due to the collection of dust/trash on photo sensors. This study was aimed towards the development of a miniature unmanned aerial system that utilized a first-person view (FPV) color camera for measuring cotton yield variability. Outcomes of the study led to the development of a method for estimating cotton yield variability from images of experimental cotton plot field taken at harvest time in 2014. These plots were treated with nitrogen fertilizer at five different rates to insure variations in cotton yield across the field. The cotton yield estimates were based on the cotton unit coverage (CUC) observed as the cotton boll image signal density. The cotton boll signals were extracted via their diffusion potential in the image intensity space. This was robust to gradients in illumination caused by cloud coverage as well as fruiting positions in the field. These estimates were provided at a much higher spatial resolution (9.0 cm2) at comparable correlations (R2=0.74) with current expensive systems. This method could prove useful for the development of low cost automated systems for cotton yield estimation as well as yield estimation systems for other crops.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:8159en
dc.identifier.urihttp://hdl.handle.net/10919/72908en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectYield Variabilityen
dc.subjectSignal Extractionen
dc.subjectYield Estimationen
dc.subjectRemote Sensingen
dc.subjectMiniature Unmanned Aerial Vehiclesen
dc.subjectZone Managementen
dc.subjectLaplacian of Gaussianen
dc.titleTowards Autonomous Cotton Yield Monitoringen
dc.typeThesisen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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