Computing that turns knowledge into impact.

In this study, we explored the application of Deep Learning techniques for the classification of coconut plantations from images collected by a Remotely Piloted Aircraft (RPA). A Convolutional Neural Network was used to create a model that classifies images into two categories: coconut and non-coconut. The model achieved an accuracy of 88%, concluding that Deep Learning techniques are viable tools for the identification of plants in large areas, with the potential to improve the management of agricultural crops.

Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperparameters. The present study presents a Framework capable of making projections from time series related to cases and deaths by COVID-19 in the Amazonian state of Para´, in Brazil. For the first time, deep learning models such as TCN, TRANSFORMER, TFT, N-BEATS, and N-HiTS were assessed for this purpose. The ARIMA statistical model was also used in post-processing for residual adjustment and short-term smoothing of the generated forecasts. The Framework generates probabilistic forecasts, with multivariate support, considering the following variables: daily cases per day of the first symptom, cases published daily, the occurrence of deaths, deaths published daily, and percentage of daily vaccination. The generated predictions are statistically evaluated by determining the best model for 7-day moving average projections using evaluating metrics such as MSE, RMSE, MAPE, sMAPE, r 2 , Coefficient of Variation, and residual analysis. As a result, the generated projections showed an average error of 5.4% for Cases Publication, 8.0% for Cases Symptoms, 11.12% for Deaths Publication, and 4.6% for Deaths Occurrence, with the N-HiTS and N-BEATS models obtaining better results. In general terms, the use of deep learning models to predict cases and deaths from COVID-19 has proven to be a valuable practice for analyzing the spread of the virus, which allows health managers to better understand and respond to this kind of pandemic outbreak.

Livestock in the territory of Pará offers planning challenges for both the state and the private sector, with consequences of herd expansion impacting economic and socio-environmental variables. This article presents a study on livestock projections in terms of production and pasture coverage area using ARIMA (AutoRegressive Integrated Moving Average) modeling through the hierarchical approach. A short-term time series data analysis framework was developed, covering data referring to the state of Pará between the years 1985 to 2021, with projections until 2026. The results indicate an increase of 19% in the number of cattle and 1.5% in pasture areas in the state of Pará, revealing an increase in productivity in the Marabá region, as well as stabilization in the Redenção region.