Objetivou-se com este trabalho avaliar a qualidade fisiológica de sementes de quatro cultivares de feijão-comum (Phaseolus vulgaris L.), produzidas nas condições edafoclimáticas de Janaúba, Norte de Minas Gerais, antes e após o armazenamento. O experimento foi conduzido no Laboratório de Análise de Sementes da Universidade Estadual de Montes Claros (Unimontes), no período de agosto de 2011 a setembro de 2012. O delineamento experimental utilizado foi inteiramente casualisado, em esquema fatorial 2 x 4, sendo dois períodos de armazenamento e quatro cultivares de feijão (Ouro Vermelho, Ouro Negro, Madrepérola e Manteigão vermelho), com quatro repetições. Após a colheita e aos doze meses de armazenamento, as sementes foram avaliadas quanto ao teor de água, massa de mil sementes, à germinação e ao vigor (primeira contagem de germinação, emergência de plântulas, e envelhecimento acelerado). Após doze meses de armazenamento, apenas as sementes da cultivar Madrepérola mantiveram uma porcentagem de germinação superior a 80%. As cultivares Ouro Vermelho e Ouro Negro apresentaram reduções de 32 e 29% na germinação, respectivamente, indicando que, possivelmente, as condições de armazenamento do presente trabalho não foram eficientes na conservação da qualidade fisiológica dessas cultivares. A qualidade fisiológica das sementes da cultivar Madrepérola não é influenciada pelo período de armazenamento. O armazenamento durante doze meses reduz a qualidade fisiológica das cultivares Ouro Vermelho e Ouro Negro. Independente do período de armazenamento, as sementes da cultivar Manteigão Vermelho apresentam qualidade fisiológica inferior.

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The objective of this study was to evaluate the potential of the practical application of unmanned aerial vehicles and RGB vegetation indices (VIs) in the monitoring of a coffee crop. The study was conducted in an experimental coffee field over a 12-month period. An RGB digital camera coupled to a UAV was used. Nine VIs were evaluated in this study. These VIs were subjected to a Pearson correlation analysis with the leaf area index (LAI), and subsequently, the VIs with higher R2 values were selected. The LAI was estimated by plant height and crown diameter values obtained by imaging, which were correlated with these values measured in the field. Among the VIs evaluated, MPRI (0.31) and GLI (0.41) presented greater correlation with LAI; however, the correlation was weak. Thematic maps of VIs in the evaluated period showed variability present in the crop. The evolution of weeds in the planting rows was noticeable with both VIs, which can help managers to make the decision to start crop management, thus saving resources. The results show that the use of low-cost UAVs and RGB cameras has potential for monitoring the coffee production cycle, providing producers with information in a more accurate, quick and simple way.

Wood density is an important criterion for material classification, as it is directly related to quality of wood for structural use. Several studies have shown promising results for the estimation of wood density by near infrared spectroscopy. However, the optimal conditions for spectral acquisition need to be investigated in order to develop predictive models and to understand how anisotropy and surface roughness affect the statistics of predictive partial least square regression models. The aim of this study was to evaluate how the spectral acquisition technique, wood surface, and the surface quality influence the ability of partial least square-based models to estimate wood density. Near infrared spectra were recorded using an integrating sphere and fiber-optic probe on the tangential, radial, and transverse surfaces machined by circular and band saws in 278 wood specimens of six-yearold Eucalyptus hybrids. The basic density values determined by the conventional method were then correlated with near infrared spectra acquired using an integrating sphere and fiber-optic probe on the wood surfaces by means of partial least square regressions. The most promising models for predicting wood density were generated from near infrared spectra obtained from the transverse surface machined by the bandsaw, via an integrating sphere (r 2 p ¼ 0:87, RMSEP ¼ 23 kg m À3 and RPD ¼ 3.0) as well as for the optic fiber (r 2 p ¼ 0:78, RMSEP ¼ 35 kg m À3 and RPD ¼ 2.1). Surface quality affected the spectral information and robustness of predictive models with a rougher surface, caused by band sawing, showing better results.

Background: Near infrared (NIR) spectroscopy has been successfully applied to estimate the chemical, physical and mechanical properties of various biological materials, including wood. This study aimed to evaluate basic density calibrations based on NIR spectra collected from three wood faces and subject to different mathematical treatments. Methods: Diffuse reflectance NIR spectra were recorded using an integrating sphere on the transverse, radial and tangential surfaces of 278 wood specimens of Eucalyptus urophylla x Eucalyptus grandis. Basic density of the wood specimens was determined in the laboratory by the immersion method and correlated with NIR spectra by Partial Least Squares regression. Different statistical treatments were then applied to the data, including Standard Normal Variate, Multiplicative Scatter Correction, First and Second Derivatives, Normalization, Autoscale and MeanCenter transformations. Results: The predictive model based on NIR spectra measured on the transverse surface performed the best (R²cv = 0.85 and RMSE = 25.5 kg/m³) while the model developed from the NIR spectra measured on the tangential surface had the poorest performance (R²cv = 0.53 and RMSE = 46.8 kg/m³). The difference in performance between models based on original (untreated) and mathematically-treated spectra was minimal. Conclusions: Multivariate models fitted to NIR spectra were found to be efficient for predicting the basic density of Eucalyptus wood, especially when based on spectra measured on the transversal surface. For this data set, models based on the original spectra and mathematically treated spectra had similar performance. The reported findings show that mathematical transformations are not always able to extract more information from the spectra in the NIR.