AgIIS, Agricultural Irrigation Imaging System, design and application

Persistent Link:
http://hdl.handle.net/10150/279836
Title:
AgIIS, Agricultural Irrigation Imaging System, design and application
Author:
Haberland, Julio Andres
Issue Date:
2001
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Remote sensing is a tool that is increasingly used in agriculture for crop management purposes. A ground-based remote sensing data acquisition system was designed, constructed, and implemented to collect high spatial and temporal resolution data in irrigated agriculture. The system was composed of a rail that mounts on a linear move irrigation machine, and a small cart that runs back and forth on the rail. The cart was equipped with a sensors package that measured reflectance in four discrete wavelengths (550 nm, 660 nm, 720 nm, and 810 nm, all 10 nm bandwidth) and an infrared thermometer. A global positioning system and triggers on the rail indicated cart position. The data was postprocessed in order to generate vegetation maps, N and water status maps and other indices relevant for site-specific crop management. A geographic information system (GIS) was used to generate images of the field on any desired day. The system was named AgIIS (A̲gricultural I̲rrigation I̲maging S̲ystem). This ground based remote sensing acquisition system was developed at the Agricultural and Biosystems Engineering Department at the University of Arizona in conjunction with the U.S. Water Conservation Laboratory in Phoenix, as part of a cooperative study primarily funded by the Idaho National Environmental and Engineering Laboratory. A second phase of the study utilized data acquired with AgIIS during the 1999 cotton growing season to model petiole nitrate (PNO₃⁻) and total leaf N. A latin square experimental design with optimal and low water and optimal and low N was used to evaluate N status under water and no water stress conditions. Multivariable models were generated with neural networks (NN) and multilinear regression (MLR). Single variable models were generated from chlorophyll meter readings (SPAD) and from the Canopy Chlorophyll Content Index (CCCI). All models were evaluated against observed PNO₃⁻ and total leaf N levels. The NN models showed the highest correlation with PNO₃⁻ and total leaf N. AgIIS was a reliable and efficient data acquisition system for research and also showed potential for use in commercial farming systems.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Agriculture, Agronomy.; Engineering, Agricultural.; Remote Sensing.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Agricultural and Biosystems Engineering
Degree Grantor:
University of Arizona
Advisor:
Waller, M. Peter

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAgIIS, Agricultural Irrigation Imaging System, design and applicationen_US
dc.creatorHaberland, Julio Andresen_US
dc.contributor.authorHaberland, Julio Andresen_US
dc.date.issued2001en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractRemote sensing is a tool that is increasingly used in agriculture for crop management purposes. A ground-based remote sensing data acquisition system was designed, constructed, and implemented to collect high spatial and temporal resolution data in irrigated agriculture. The system was composed of a rail that mounts on a linear move irrigation machine, and a small cart that runs back and forth on the rail. The cart was equipped with a sensors package that measured reflectance in four discrete wavelengths (550 nm, 660 nm, 720 nm, and 810 nm, all 10 nm bandwidth) and an infrared thermometer. A global positioning system and triggers on the rail indicated cart position. The data was postprocessed in order to generate vegetation maps, N and water status maps and other indices relevant for site-specific crop management. A geographic information system (GIS) was used to generate images of the field on any desired day. The system was named AgIIS (A̲gricultural I̲rrigation I̲maging S̲ystem). This ground based remote sensing acquisition system was developed at the Agricultural and Biosystems Engineering Department at the University of Arizona in conjunction with the U.S. Water Conservation Laboratory in Phoenix, as part of a cooperative study primarily funded by the Idaho National Environmental and Engineering Laboratory. A second phase of the study utilized data acquired with AgIIS during the 1999 cotton growing season to model petiole nitrate (PNO₃⁻) and total leaf N. A latin square experimental design with optimal and low water and optimal and low N was used to evaluate N status under water and no water stress conditions. Multivariable models were generated with neural networks (NN) and multilinear regression (MLR). Single variable models were generated from chlorophyll meter readings (SPAD) and from the Canopy Chlorophyll Content Index (CCCI). All models were evaluated against observed PNO₃⁻ and total leaf N levels. The NN models showed the highest correlation with PNO₃⁻ and total leaf N. AgIIS was a reliable and efficient data acquisition system for research and also showed potential for use in commercial farming systems.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectAgriculture, Agronomy.en_US
dc.subjectEngineering, Agricultural.en_US
dc.subjectRemote Sensing.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineAgricultural and Biosystems Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorWaller, M. Peteren_US
dc.identifier.proquest3026570en_US
dc.identifier.bibrecord.b42177625en_US
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