Dentre algumas fontes renováveis de energia, destacam-se os ventos. No Brasil, em especial na região Nordeste, tem-se observado avanços quanto aos estudos e investimentos em localidades consideradas potenciais produtoras de energia eólica. Neste contexto, podem ser empregadas as funções densidade de probabilidade de modelos de distribuição como forma auxiliar à tomada de decisão sobre a escolha de uma determinada região para a instalação de parques eólicos. Neste trabalho, buscou-se analisar o potencial eólico para geração de energia proveniente dos ventos em Petrolina-PE, com a série histórica de velocidade do vento de 01/01/2015 a 31/12/2016 e através da comparação entre os ajustes realizados pelas distribuições Weibull com dois parâmetros (Weibull-2p) e Burr, bem como a partir da análise da velocidade média do vento verificada na região. Além disso, observar a direção predominante dos ventos por meio da Rosa dos Ventos. Para a estimação dos parâmetros das distribuições, foi adotado o Método da Máxima Verossimilhança (MMV) que tem alcançado valores ótimos em relação a outros métodos de estimativa de parâmetros. Os critérios de seleção AIC e BIC, a estatística de Anderson-Darling e as acurácias MAPE e MAD foram adotadas para a avaliação da bondade dos ajustes das distribuições, onde se verificou que a Weibull-2p forneceu melhor modelagem aos dados analisados. A direção predominante dos ventos encontrada foi a sudeste, com variação entre ~105º e ~135º e velocidade média de 8,4m/s. Com os resultados obtidos, a região estudada alcançou, segundo classificação do NREL, avaliação esplêndida para a viabilidade de geração de energia eólica.

Anticipating the ornamental quality of plants is of significant importance for genetic breeding programs. This study investigated the potential of predicting and classifying whether ornamental pepper plants will exhibit desirable ornamental traits based on RGB images, comparing these results with an approach relying on morphological measurements. To achieve this, pepper plants from fifteen accessions were cultivated, and photographs were taken weekly throughout their growth cycle until fruit maturation. A Vision Transformer (ViT)-based model was employed to predict the suitability of the plants for ornamental purposes, and its predictions were validated against assessments conducted by eight experts. An XGBoost-based classifier was employed as well for estimations based on morphological measurements with an accuracy over 92%. The results showed that the ornamental suitability of plants can be accurately estimated and predicted up to seven weeks in advance from photos, with accuracy over 80%. Interestingly, higher-resolution RGB images did not significantly improve the accuracy of the ViT model. Furthermore, the estimation of ornamental potential using morphological measurements and RGB images yielded similar accuracy, indicating that a single photograph can effectively replace costly and time-consuming morphological measurements. As far as the authors are aware, this work is the first to forecast the ornamental potential of pepper plants (Capsicum spp.) multiple weeks ahead of time using image-based deep learning models.

Being capable of accurately predicting morphological parameters of the plant weeks before achieving fruit maturation is of great importance in the production and selection of suitable ornamental pepper plants. The objective of this article is evaluating the feasibility and assessing the performance of CNN-based models using RGB images as input to forecast two morphological parameters: plant height and canopy diameter. To this end, four CNN-based models are proposed to predict these morphological parameters in four different scenarios: first, using as input a single image of the plant; second, using as input several images from different viewpoints of the plant acquired on the same date; third, using as input two images from two consecutive weeks; and fourth, using as input a set of images consisting of one image from each week up to the current date. The results show that it is possible to accurately predict both plant height and canopy diameter. The RMSE for a forecast performed 6 weeks in advance to the actual measurements was below 4.5 cm and 4.2 cm, respectively. When information from previous weeks is added to the model, better results can be achieved and as the prediction date gets closer to the assessment date the accuracy improves as well.

The extractivism of Dimorphandra mollis Benth., which is a native tree from the Brazilian Cerrado biome, popularly known as fava d’anta, combined with the reduction in native vegetation area in the country over the years may result in a decrease in the specie’s natural populations. The objective of this study was to estimate the quantitative genetic parameters in nursery, hardening, and field phases, based on a progeny test. The experimental design adopted was randomized blocks (six blocks for the nursery and hardening phases, and four blocks for the field phase with 5 plants/plot and 72 mother trees), with evaluations of the collar diameter and seedling height at 30, 90, 150, 480, and 570 days after sowing, between the production and planting phases. Among the coefficients of variance, the phenotypic and additive ones showed the highest values. Heritabilities for height ranged from moderate to high (0.15 to 0.43), indicating good genetic control of the traits, high potential for selection, and possibility of genetic gains. The genetic divergence of the progenies resulted in division into five groups, confirming the existence of genetic variability among the evaluated progenies and the potential for conservation and breeding programs.