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京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18280号 / 工博第3872号 / 新制||工||1594(附属図書館) / 31138 / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 小林 哲生, 教授 中村 裕一, 准教授 古谷 栄光 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

One 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. / Master of Science

Made available in DSpace on 2016-08-17T14:53:18Z (GMT). No. of bitstreams: 1 Dissertacao Ewaldo.pdf: 1169300 bytes, checksum: fc5d4b9840bbafe39d03cd1221da615e (MD5) Previous issue date: 2012-02-10 / This work derives and evaluates a nonlinear method for Blind Source Extraction (BSE) in a Reproducing Kernel Hilbert Space (RKHS) framework. For extracting the desired signal from a mixture a priori information about the autocorrelation function of that signal translated in a linear transformation of the Gram matrix of the nonlinearly transformed data to the Hilbert space. Our method proved to be more robust than methods presented in the literature of BSE with respect to ambiguities in the available a priori information of the signal to be extracted. The approach here introduced can also be seen as a generalization of Kernel Principal Component Analysis to analyze autocorrelation matrices at specific time lags. Henceforth, the method here presented is a kernelization of Dependent Component Analysis, it will be called Kernel Dependent Component Analysis (KDCA). Also in this dissertation it will be show a Information-Theoretic Learning perspective of the analysis, this will study the transformations in the extracted signals probability density functions while linear operations calculated in the RKHS. / Esta dissertação deriva e avalia um novo método nãolinear para Extração Cega de Sinais através de operações algébricas em um Espaço de Hilbert Reproduzido por Kernel (RKHS, do inglês Reproducing Kernel Hilbert Space). O processo de extração de sinais desejados de misturas é realizado utilizando-se informação sobre a estrutura temporal deste sinal desejado. No presente trabalho, esta informação temporal será utilizada para realizar uma transformação linear na matriz de Gram das misturas transformadas para o espaço de Hilbert. Aqui, mostrarse- á também que o método proposto é mais robusto, com relação a ambigüidades sobre a informação temporal do sinal desejado, que aqueles previamente apresentados na literatura para realizar a mesma operação de extração. A abordagem estudada a seguir pode ser vista como uma generalização da Análise de Componentes Principais utilizando Kerneis para analisar matriz de autocorrelação dos dados para um atraso específico. Sendo também uma kernelização da Análise de Componentes Dependentes, o método aqui desenvolvido é denominado Análise de Componentes Dependentes utilizando Kerneis (KDCA, do inglês Kernel Dependent Component Analysis). Também será abordada nesta dissertação, a perspectiva da Aprendizagem de Máquina utilizando Teoria da Informação do novo método apresentado, mostrando assim, que transformações são realizadas na função densidade de probabilidade do sinal extraído enquanto que operação lineares são calculadas no RKHS.