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Master's Dissertation
DOI
Document
Author
Full name
Miller Andres Ruiz Sánchez
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2019
Supervisor
Committee
Fiorio, Peterson Ricardo (President)
Barros, Pedro Paulo da Silva
Mendonça, Fernando Campos
Nakai, Érica Silva
Title in Portuguese
Resposta hiperespectral de folha na diferenciação de adubação nitrogenada e predição do teor de clorofila na cultura de capim 'Mombaça'
Keywords in Portuguese
Megathyrsus maximus
Análise linear discriminante
Clorofila
Machine learning
Reflectância espectral
Abstract in Portuguese
A atividade bovina do Brasil é uma das maiores do mundo, devido principalmente, ao uso predominante de pastagens tropicais na dieta do gado, porém, o alto custo dos insumos, com destaque para a adubação nitrogenada, dificulta a obtenção de altas produções de forragens. Por esta razão, a utilização de novas tecnologias que permitam melhorar o manejo e aumentar o rendimento de culturas como o capim 'Mombaça', por meio do uso racional dos fatores de produção, é de alta relevância. O objetivo deste estudo foi avaliar o uso de dados hiperespectrais para a discriminação de diferentes tratamentos de adubação nitrogenada, e comparar diferentes métodos para obter valores de concentração de clorofila a partir de assinaturas espectrais de capim 'Mombaça'. Foram estabelecidos quatro tratamentos de com doses diferentes de adubação nitrogenada. Foi medida a reflectância espectral de folhas coletadas em cada tratamento por meio de um sensor hiperespectral em laboratório além de medições de clorofila das folhas. Foi encontrado que as assinaturas espectrais de cada tratamento tiveram comportamentos diferentes, principalmente nas regiões do verde e do red-edge, sendo que tais diferenças dependeram da quantidade de adubo nitrogenado aplicado e do teor de clorofila foliar. A separação dos tratamentos foi possível mediante o uso de análise linear discriminante, baseando-se nos dados de reflectância espectral obtidos em cada tratamento. A obtenção de valores de concentração de clorofila da folha por meio de reflectância espectral foi possível por meio de técnicas de machine learning, destacando-se o support vector machine, como a melhor alternativa. As regiões do red-edge e do verde foram as mais influentes para realizar o cálculo dos valores de concentração de clorofila na folha.
Title in English
Hyperspectal response of leaf on the differentiation of nitrogen fertilization and prediction of chlorophyll content in 'Mombaça' grass
Keywords in English
Megathyrsus maximus
Chlorophyll
Linear discriminant analyisis
Machine learning
Spectral reflectance
Abstract in English
Brazil's cattle production is one of the largest in the world, due mainly, to the predominant use of tropical pastures in the cattle diet. However, the high cost of inputs, especially nitrogen fertilizer, makes it difficult to obtain high forage production, for this reason, the use of new technologies to improve the management and to boost the yield of crops such as Mombaça grass, through the rational use of resources, is of high relevance. The aim of this study was to evaluate the use of hyperspectral data to discriminate different treatments of nitrogen fertilization, and to compare different methods to obtain chlorophyll values of chlorophyll concentration based on spectral signatures of 'Mombaça' grass. Four treatments were established with different doses of nitrogen fertilization. The spectral reflectance of leaves collected in each treatment was measured with a hyperspectral sensor, and leaf chlorophyll measurements were performed, both, on laboratory conditions. It was found that the spectral signatures of each treatments had different behaviors, mainly in the green and red-edge regions, and such differences depended on the amount of nitrogen fertilizer applied and the chlorophyll content of the leaf. The separation of the treatments was possible through the use of linear discriminant analysis, based on the spectral reflectance data obtained in each treatment. Retrieve chlorophyll concentration values from the leaf spectral reflectance was possible by the use of machine learning techniques, highlighting the support vector machine as the best alternative. The red-edge and green regions were the most influential in calculating the values of leaf chlorophyll concentration.
 
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Publishing Date
2019-07-25
 
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