Influence of nitrogen (N) deficiency on castor cv. ‘Hale’ growth, physiology, and leaf reflectance properties were investigated. Treatments imposed were complete Hoagland’s nutrient solution (control, 100N), reduced N to 20% of the control (20N) and withheld N from the solution (0N) from 34 to 66 days after sowing (DAS) in 12-L pots grown out doors. N deficiency significantly reduced leaf area, chlorophyll and photosynthesis resulting lower total biomass. Leaf and stem growth rates were more sensitive to leaf N concentration than photosynthesis and leaf addition rates. N deficiency stress increased leaf reflectance at R555 and R715 nm and caused a red-edge shift to shorter wavelengths. Reflectance ratios of R455/R605 and R505/R605 nm was highly correlated with leaf N on weight (r2 = 0.93) and area-based (r2 = 0.90) estimations, respectively. Similarly, reflectance ratio R635/R505 was highly correlated with chlorophylls (r2 = 0.94). The N-specific wavebands and functional relationships between leaf N and growth and developmental processes would be useful for rapid and non-destructive estimation of leaf N and growth rates of castor.

The use of nitrogenous fertilizers in agricultural and forestry practices coupled with increased fossil fuel combustion and resulting nitrogen (N) deposition across the landscape have contributed to a near doubling of N inputs to terrestrial ecosystems.  With such dramatic changes have come adverse environmental consequences including the acidification of soil and water resources and an increased rate of biodiversity loss in both flora and fauna.  A method of rapidly predicting ecosystem susceptibility to N loss across large spatial scales would facilitate the identification of those systems most likely to contribute to potentially adverse environmental impacts.  To begin the development of such a framework, this research utilizes study sites located throughout the geographic ranges of Douglas-fir (Pseudotsuga menziesii) and loblolly pine (Pinus taeda) to explore relationships between hyperspectral remote sensing, N stable isotope ratios ("15N) and growth response to nitrogenous fertilizer.  In both species multiple linear regression models relating leaf-level reflectance to "15N showed strong predictive capabilities, with some models explaining more than 65% of the variance in "15N.  Significant correlations between "15N metrics and growth response to N fertilization were also observed in both species.  Additional exploratory analysis of the inclusion of "15N metrics with other environmental and edaphic variables to predict fertilizer growth response showed an increase in model performance with the addition of the enrichment factor (EF ="15NFol - "15NSoil).  This research demonstrates the ability of hyperspectral reflectance to predict "15N and reveals the potential of "15N to be included in future models to predict fertilizer growth response. / Master of Science

A utilização de dados provenientes do sensoriamento remoto é alternativa para otimizar a utilização de insumos, dentre eles o nitrogênio. O presente trabalho teve como objetivo verificar a possibilidade de uso de um sensor hiperespectral em dossel na cultura da cana-de-açúcar, verificando sua capacidade em discriminar a resposta da cultura as diferentes doses de nitrogênio e estimar o teor foliar de nitrogênio, em três áreas experimentais. O trabalho foi dividido em três capítulos: O primeiro capitulo utiliza os dados hiperespectrais somente da variedade SP 81-3250, única comum em todas as áreas, de todas as datas de coleta das três áreas experimentais para verificar o potencial dos dados em diferenciar as doses de nitrogênio aplicado (0, 50, 100 e 150 kg.ha-1) e qual melhor época. Os dados espectrais foram avaliados pela estatística multivariada da análise discriminante, em que os centroides das diferentes doses foram submetidos a análise de variância. Os resultados obtidos foram que os meses de dezembro, janeiro e fevereiro discriminou todas as doses nas três áreas, o mesmo não ocorreu no mês de agosto. As bandas que apresentaram maiores significância foram na região do verde, red-edge e infravermelho próximo. No segundo capitulo foi avaliado a sensibilidade dos dados hiperespectrais em estimar a biomassa do ponteiro da cana-de-açúcar. Para isso foi utilizado somente os dados de Piracicaba. A análise espectral foi realizada aos 137, 169 e 193 Dias Após o Corte (DAC) e a avaliação biométrica foi realizada aos 345 DAC. Durante o corte de dois metros de linha, realizado manualmente. A biomassa do ponteiro foi submetida ao teste de Shapiro-Wilk, análise de variância pelo Teste F e as médias quando significativas, comparadas pelo Teste de Tukey. Posteriormente foi realizada a análise de correlação de Pearson da biomassa do ponteiro e cada comprimento de onda. Análise mostrou que existe correlação positiva entre a biomassa do ponteiro e a reflectância do dossel aos 137 DAC e 169 DAC, porém aos 193 DAC não houve nenhum comprimento de onda com correlação significativa. O comprimento de onda de 685 nm aos 137 DAC obteve a maior correlação, de 0,33. No terceiro capitulo teve por objetivo selecionar variáveis a partir de dados hiperespectrais de dossel da cana-de-açúcar para geração de modelos para predição do Teor Foliar de Nitrogênio. Para isso foi utilizado os dados das três áreas experimentais, que receberam doses de 0, 50, 100 e 150 kg.ha-1 de nitrogênio. Para redução da dimensionalidade dos dados foi utilizada a metodologia sparse Partial Least Square (sPLS), posteriormente foi feito a combinação linear das variáveis selecionadas, por meio de Regressão Linear Múltipla por Stepwise (SMLR). O modelo geral teve valores de R² ajustado e RMSE respectivamente de 0,50 e 1,67 g kg-1. Os modelos gerados para Piracicaba, Jaú e Santa Maria obtiveram R² ajustado, respectivamente, de 0,31, 0,53 e 0,54. Sensores hiperespectrais de dossel podem ser utilizados para predição do TFN e monitoramento de aplicação de nitrogênio em cana-de-açúcar. / The use of data from remote sensing is an alternative to optimize the use of agricultural inputs, including nitrogen. The present study aimed to verify the possibility of using a hyperspectral sensor in sugarcane canopy, verifying its ability to discriminate crop response to different rates of nitrogen and estimating leaf nitrogen content in three experimental areas. The work is divided in three chapters: The first chapter uses hyperspectral data of the variety SP 81-3250, which is the only one present in all the areas for all dates of collection in three of experimental areas, to check the potential of the data and the best time to differentiate between rates of nitrogen (0, 50, 100 and 150 kg.ha-1). Spectral data were evaluated by multivariate discriminant analysis, wherein the centroids of the rates were submitted to an Analysis of Variance. The results showed that the all doses in three areas of study were discriminated for the months of December, January and February, but the same thing hasn\'t happened in the month of August. The bands that showed statistically significant power difference were found in the green, red, and near-infrared edge spectral regions. In the second chapter, the sensitivity of hyperspectral data was evaluated to estimate the sugarcane biomass (pointes) for the data from Piracicaba. Spectral analysis was performed at 137, 169 and 193 Days After Harvest (DAH) and evaluation of sugarcane yield was performed 345 DAH. Biomass was analyzed using The Shapiro-Wilk test of normality, F test (analysis of variance), respectively, and when significant, compared by the Tukey test. Biomass (pointer) and each wavelength were analyzed by Pearson\'s correlation analysis. The results showed that there is a positive correlation between biomass (pointer) and the canopy reflectance to 137 DAH and 169 DAH, however there was no wavelength with a significant correlation to 193 DAH. The best power relationship was obtained at 685 nm, at 137 days. The third chapter aimed to select variables from hyperspectral data of sugarcane canopy to generate models for prediction of Foliar Nitrogen Content, for three experimental areas that received nitrogen rates (0, 50, 100 and 150 kg.ha-1). Sparse Partial Least Square (sPLS) was used to reduce the dimensionality of the data. Subsequently, the linear combination of selected variables was done through Stepwise Multiple Linear Regression (SMLR). The RMSE and adjusted R-squared statistics were 0.50 and 1.67 g.kg-1, respectively. The models to Piracicaba, Jaú and Santa Maria presented adjusted R-squared 0.31, 0.53, and 0.54, respectively. Hyperspectral sensors for canopy can be used for prediction of the TFN and monitoring of nitrogen application in sugarcane.

A utilização de dados provenientes do sensoriamento remoto é alternativa para otimizar a utilização de insumos, dentre eles o nitrogênio. O presente trabalho teve como objetivo verificar a possibilidade de uso de um sensor hiperespectral em dossel na cultura da cana-de-açúcar, verificando sua capacidade em discriminar a resposta da cultura as diferentes doses de nitrogênio e estimar o teor foliar de nitrogênio, em três áreas experimentais. O trabalho foi dividido em três capítulos: O primeiro capitulo utiliza os dados hiperespectrais somente da variedade SP 81-3250, única comum em todas as áreas, de todas as datas de coleta das três áreas experimentais para verificar o potencial dos dados em diferenciar as doses de nitrogênio aplicado (0, 50, 100 e 150 kg.ha-1) e qual melhor época. Os dados espectrais foram avaliados pela estatística multivariada da análise discriminante, em que os centroides das diferentes doses foram submetidos a análise de variância. Os resultados obtidos foram que os meses de dezembro, janeiro e fevereiro discriminou todas as doses nas três áreas, o mesmo não ocorreu no mês de agosto. As bandas que apresentaram maiores significância foram na região do verde, red-edge e infravermelho próximo. No segundo capitulo foi avaliado a sensibilidade dos dados hiperespectrais em estimar a biomassa do ponteiro da cana-de-açúcar. Para isso foi utilizado somente os dados de Piracicaba. A análise espectral foi realizada aos 137, 169 e 193 Dias Após o Corte (DAC) e a avaliação biométrica foi realizada aos 345 DAC. Durante o corte de dois metros de linha, realizado manualmente. A biomassa do ponteiro foi submetida ao teste de Shapiro-Wilk, análise de variância pelo Teste F e as médias quando significativas, comparadas pelo Teste de Tukey. Posteriormente foi realizada a análise de correlação de Pearson da biomassa do ponteiro e cada comprimento de onda. Análise mostrou que existe correlação positiva entre a biomassa do ponteiro e a reflectância do dossel aos 137 DAC e 169 DAC, porém aos 193 DAC não houve nenhum comprimento de onda com correlação significativa. O comprimento de onda de 685 nm aos 137 DAC obteve a maior correlação, de 0,33. No terceiro capitulo teve por objetivo selecionar variáveis a partir de dados hiperespectrais de dossel da cana-de-açúcar para geração de modelos para predição do Teor Foliar de Nitrogênio. Para isso foi utilizado os dados das três áreas experimentais, que receberam doses de 0, 50, 100 e 150 kg.ha-1 de nitrogênio. Para redução da dimensionalidade dos dados foi utilizada a metodologia sparse Partial Least Square (sPLS), posteriormente foi feito a combinação linear das variáveis selecionadas, por meio de Regressão Linear Múltipla por Stepwise (SMLR). O modelo geral teve valores de R² ajustado e RMSE respectivamente de 0,50 e 1,67 g kg-1. Os modelos gerados para Piracicaba, Jaú e Santa Maria obtiveram R² ajustado, respectivamente, de 0,31, 0,53 e 0,54. Sensores hiperespectrais de dossel podem ser utilizados para predição do TFN e monitoramento de aplicação de nitrogênio em cana-de-açúcar. / The use of data from remote sensing is an alternative to optimize the use of agricultural inputs, including nitrogen. The present study aimed to verify the possibility of using a hyperspectral sensor in sugarcane canopy, verifying its ability to discriminate crop response to different rates of nitrogen and estimating leaf nitrogen content in three experimental areas. The work is divided in three chapters: The first chapter uses hyperspectral data of the variety SP 81-3250, which is the only one present in all the areas for all dates of collection in three of experimental areas, to check the potential of the data and the best time to differentiate between rates of nitrogen (0, 50, 100 and 150 kg.ha-1). Spectral data were evaluated by multivariate discriminant analysis, wherein the centroids of the rates were submitted to an Analysis of Variance. The results showed that the all doses in three areas of study were discriminated for the months of December, January and February, but the same thing hasn\'t happened in the month of August. The bands that showed statistically significant power difference were found in the green, red, and near-infrared edge spectral regions. In the second chapter, the sensitivity of hyperspectral data was evaluated to estimate the sugarcane biomass (pointes) for the data from Piracicaba. Spectral analysis was performed at 137, 169 and 193 Days After Harvest (DAH) and evaluation of sugarcane yield was performed 345 DAH. Biomass was analyzed using The Shapiro-Wilk test of normality, F test (analysis of variance), respectively, and when significant, compared by the Tukey test. Biomass (pointer) and each wavelength were analyzed by Pearson\'s correlation analysis. The results showed that there is a positive correlation between biomass (pointer) and the canopy reflectance to 137 DAH and 169 DAH, however there was no wavelength with a significant correlation to 193 DAH. The best power relationship was obtained at 685 nm, at 137 days. The third chapter aimed to select variables from hyperspectral data of sugarcane canopy to generate models for prediction of Foliar Nitrogen Content, for three experimental areas that received nitrogen rates (0, 50, 100 and 150 kg.ha-1). Sparse Partial Least Square (sPLS) was used to reduce the dimensionality of the data. Subsequently, the linear combination of selected variables was done through Stepwise Multiple Linear Regression (SMLR). The RMSE and adjusted R-squared statistics were 0.50 and 1.67 g.kg-1, respectively. The models to Piracicaba, Jaú and Santa Maria presented adjusted R-squared 0.31, 0.53, and 0.54, respectively. Hyperspectral sensors for canopy can be used for prediction of the TFN and monitoring of nitrogen application in sugarcane.

Whole genome duplication, or polyploidy, is a common process in plants by which failures in meiosis or fertilization result in offspring with twice the number of chromosomes. This doubles the number of copies of every gene, an effect thought to generate new ‘raw material' upon which natural selection can act. Few studies exist examining the consequences of polyploidy for plant physiological traits. Doubling the number of gene copies may have unknown effects on leaf structure and function. In this study, I compare diploid, tetraploid, and hexaploid species within the genus Helianthus (wild sunflowers). Forty different accessions of wild sunflowers were grown under standardized greenhouse conditions and phenotyped for both leaf functional traits and leaf hyperspectral reflectance. Interestingly, I find that whole genome duplication can have effects on leaf functional traits relevant to both size and ecophysiology, and thus that polyploidy may lead to functional trait differentiation between polyploids and their diploid progenitors.

No description available.

At least 110 million landmines have been planted since the 1970s in about 70 nations, many of which remain in place today. Some risk of detection may be mitigated using currently available remote sensing techniques when vegetation is present. My study focused on using plants as phytosensors to detect buried explosives. I exposed three species representing different functional types (Cyperus esculentus (sedge), Ulmus alata (tree), Vitis labrusca (vine)) to 500 mg kg-1 of Composition B (Comp B; 60/40 mixture of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and 2,4,6-trinitrotoluene (TNT)), a commonly used explosive mixture, and measured functional traits and reflectance over a nine-week period. Cyperus esculentus was not a good indicator for the presence of explosive compounds. Comp B treatment woody species, U. alata and V. labrusca, exhibited changes in pigment content, leaf area, specific leaf area, dry leaf biomass, and canopy reflectance. The efficacy of plants as landmine detectors is species and/or functional group dependent.

Methods for rapid, safe and effective detection of unmapped buried ordnance are vital to the protection of humans and environmental quality throughout the world. This study aimed to investigate the use of phytosensing and to understand the physiological response of woody plants to 2,4,6-trinitrotoluene (TNT) contamination. Baccharis halimifolia were potted in soils containing various concentrations of TNT and physiological responses were observed over a 9-week experimental period. Measurements included the collection of remotely sensed data, such as hyperspectral reflectance and chlorophyll fluorescence, and traditional plant-level physiological data. In accordance with the hypothesis, low levels of TNT improved physiological response in plants due to the slight increase in nitrogen, while high levels of TNT induced stress. Key markers in stress responses were identified, specifically with reflectance indices and derivatives, which may separate TNT-contaminated plants from naturally stressed plants and would allow for accurate detection of buried ordnance at the landscape level.

<p>Hyperspectral imaging is widely used for obtaining optical information of light absorbers (e.g. biochemical composition) in a variety of specimens or tissues in a label-free manner. Acquiring and processing spectral data using hyperspectral imaging usually requires advanced instrumentation such as spectrometers, spectrographs or tunable color filters, which are not easily adaptable in developing instrumentation for field-based applications. Also, use of only RGB information from conventional cameras is not sufficient to obtain a reliable correlation with the actual content of the analyte of interest. We propose a new concept of ‘virtual hyperspectral imaging’ to reconstruct the full reflectance spectra from RGB image data. This allows us to use only RGB image data to determine detailed spatial distributions of analytes of interest. More importantly, it simplifies instrumentation without requiring bulky and expensive hardware. Using a data-driven approach, we apply multivariate regression to reconstruct hyperspectral reflectance image data from RGB images obtained using a conventional camera or a smartphone. </p> <p> </p> <p>In developing a reliable reconstruction matrix, it is critical to obtain a training data set of the specimen of study under the same optical geometry since the spectral reflectance and absorbance is sensitive to the detection and illumination parameters. We designed an image-guided hyperspectral system that can acquire both hyperspectral reflectance and RGB data sets under the same imaging configuration to minimize any discrepancies in the hyperspectral reflectance data acquired using different optical sensing geometries. In our technology development, a telecentric lens that is commonly used in machine vision systems but rarely in bioimaging, serves as a key component for reducing unwanted scattering in biological tissue due to its highly anisotropic scattering properties, by acting as a back-directional gating component to suppress diffuse light. We evaluate our spectrometer-less reflectance imaging method using RGB-based hyperspectral reconstruction algorithm for integration into a smartphone application for non-invasive hemoglobin analysis for anemia risk assessment in communities with limited access to central laboratory tests.</p>

Hyperspectral imaging has become one of the most popular technologies in plant phenotyping because it can efficiently and accurately predict numerous plant physiological features such as plant biomass, leaf moisture content, and chlorophyll content. Various hyperspectral imaging systems have been deployed in both greenhouse and field phenotyping activities. However, the hyperspectral imaging quality is severely affected by the continuously changing environmental conditions such as cloud cover, temperature and wind speed that induce noise in plant spectral data. Eliminating these environmental effects to improve imaging quality is critically important. In this thesis, two approaches were taken to address the imaging noise issue in greenhouse and field separately. First, a computational simulation model was built to simulate the greenhouse microclimate changes (such as the temperature and radiation distributions) through a 24-hour cycle in a research greenhouse. The simulated results were used to optimize the movement of an automated conveyor in the greenhouse: the plants were shuffled with the conveyor system with optimized frequency and distance to provide uniform growing conditions such as temperature and lighting intensity for each individual plant. The results showed the variance of the plants’ phenotyping feature measurements decreased significantly (i.e., by up to 83% in plant canopy size) in this conveyor greenhouse. Secondly, the environmental effects (i.e., sun radiation) on <a>aerial </a>hyperspectral images in field plant phenotyping were investigated and modeled. <a>An artificial neural network (ANN) method was proposed to model the relationship between the image variation and environmental changes. Before the 2019 field test, a gantry system was designed and constructed to repeatedly collect time-series hyperspectral images with 2.5 minutes intervals of the corn plants under varying environmental conditions, which included sun radiation, solar zenith angle, diurnal time, humidity, temperature and wind speed. Over 8,000 hyperspectral images of </a>corn (<i>Zea mays </i>L.) were collected with synchronized environmental data throughout the 2019 growing season. The models trained with the proposed ANN method were able to accurately predict the variations in imaging results (i.e., 82.3% for NDVI) caused by the changing environments. Thus, the ANN method can be used by remote sensing professionals to adjust or correct raw imaging data for changing environments to improve plant characterization.