Radiative transport in plant canopies: Forward and inverse problem for UAV applications

Persistent Link:
http://hdl.handle.net/10150/280700
Title:
Radiative transport in plant canopies: Forward and inverse problem for UAV applications
Author:
Furfaro, Roberto
Issue Date:
2004
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:
This dissertation deals with modeling the radiative regime in vegetation canopies and the possible remote sensing applications derived by solving the forward and inverse canopy transport equation. The aim of the research is to develop a methodology (called "end-to-end problem solution") that, starting from first principles describing the interaction between light and vegetation, constructs, as the final product, a tool that analyzes remote sensing data for precision agriculture (ripeness prediction). The procedure begins by defining the equations that describe the transport of photons inside the leaf and within the canopy. The resulting integro-differential equations are numerically integrated by adapting the conventional discrete-ordinate methods to compute the reflectance at the top of the canopy. The canopy transport equation is also analyzed to explore its spectral properties. The goal here is to apply Case's method to determine eigenvalues and eigenfunctions and to prove completeness. A model inversion is attempted by using neural network algorithms. Using input-outputs generated by running the forward model, a neural network is trained to learn the inverse map. The model-based neural network represents the end product of the overall procedure. During Oct 2002, an Unmanned Aerial Vehicles (UAVs) equipped with a camera system, flew over Kauai to take images of coffee field plantations. Our goal is to predict the amount of ripe coffee cherries for optimal harvesting. The Leaf-Canopy model was modified to include cherries as absorbing and scattering elements and two classes of neural networks were trained on the model to learn the relationship between reflectance and percentage of ripe, over-ripe and under-ripe cherries. The neural networks are interfaced with images coming from Kauai to predict ripeness percentage. Both ground and airborne images are considered. The latter were taken from the on-board Helios UAV camera system flying over the Kauai coffee field. The results are compared against hand counts and parchment data to evaluate the network performances on real applications. In ground images, the error is always less than 11%. In airborne image, the error bound is 20%. The results are certainly adequate and show the tremendous potential of the methodology.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Aerospace.; Engineering, Agricultural.; Remote Sensing.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Aerospace and Mechanical Engineering
Degree Grantor:
University of Arizona
Advisor:
Ganapol, Barry D.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleRadiative transport in plant canopies: Forward and inverse problem for UAV applicationsen_US
dc.creatorFurfaro, Robertoen_US
dc.contributor.authorFurfaro, Robertoen_US
dc.date.issued2004en_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.abstractThis dissertation deals with modeling the radiative regime in vegetation canopies and the possible remote sensing applications derived by solving the forward and inverse canopy transport equation. The aim of the research is to develop a methodology (called "end-to-end problem solution") that, starting from first principles describing the interaction between light and vegetation, constructs, as the final product, a tool that analyzes remote sensing data for precision agriculture (ripeness prediction). The procedure begins by defining the equations that describe the transport of photons inside the leaf and within the canopy. The resulting integro-differential equations are numerically integrated by adapting the conventional discrete-ordinate methods to compute the reflectance at the top of the canopy. The canopy transport equation is also analyzed to explore its spectral properties. The goal here is to apply Case's method to determine eigenvalues and eigenfunctions and to prove completeness. A model inversion is attempted by using neural network algorithms. Using input-outputs generated by running the forward model, a neural network is trained to learn the inverse map. The model-based neural network represents the end product of the overall procedure. During Oct 2002, an Unmanned Aerial Vehicles (UAVs) equipped with a camera system, flew over Kauai to take images of coffee field plantations. Our goal is to predict the amount of ripe coffee cherries for optimal harvesting. The Leaf-Canopy model was modified to include cherries as absorbing and scattering elements and two classes of neural networks were trained on the model to learn the relationship between reflectance and percentage of ripe, over-ripe and under-ripe cherries. The neural networks are interfaced with images coming from Kauai to predict ripeness percentage. Both ground and airborne images are considered. The latter were taken from the on-board Helios UAV camera system flying over the Kauai coffee field. The results are compared against hand counts and parchment data to evaluate the network performances on real applications. In ground images, the error is always less than 11%. In airborne image, the error bound is 20%. The results are certainly adequate and show the tremendous potential of the methodology.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Aerospace.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.disciplineAerospace and Mechanical Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGanapol, Barry D.en_US
dc.identifier.proquest3158094en_US
dc.identifier.bibrecord.b48126585en_US
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