Anais da Academia Brasileira de Ciências
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/13096
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Item Seasonal behavior of vegetation determined by sensor on an unmanned aerial vehicle(Academia Brasileira de Ciências, 2021-04-09) Felix, Filipe C.; Avalos, Fabio A.P.; Lima, Wellington De; Cândido, Bernardo M.; Silva, Marx L.N.; Mincato, Ronaldo L.Geographic information systems make it possible to obtain fine scale maps for environmental monitoring from airborne sensors on aerial platforms, such as unmanned aerial vehicles (UAVs), which offer products with low costs and high space-time resolution. The present study assessed the performance of an UAV in the evaluation of the seasonal behavior of five vegetation coverages: Coffea spp., Eucalyptus spp., Pinus spp. and two forest remnants. For this, vegetation indices (Excess Green and Excess Red minus Green), meteorological data and moisture of surface soils were used. In addition, Sentinel-2 satellite images were used to validate these results. The highest correlations with soil moisture were found in coffee and Forest Remnant 1. The Coffea spp. had the indices with the highest correlation to the studied soil properties. However, the UAV images also provided relevant results for understanding the dynamics of forest remnants. The Excess Green index (p = 0.96) had the highest correlation coefficients for Coffea spp., while the Excess Red minus Green index was the best index for forest remnants (p = 0.75). The results confirmed that low-cost UAVs have the potential to be used as a support tool for phenological studies and can also validate satellite-derived data.Item Classifiers based on artificial intelligence in the prediction of recently planted coffee cultivars using a Remotely Piloted Aircraft System(Academia Brasileira de Ciências, 2023-11-03) Bento, Nicole L.; Ferraz, Gabriel Araújo E.S.; Barata, Rafael Alexandre P.; Soares, Daniel V.; Teodoro, Sabrina A.; Estima, Pedro Henrique De O.The classification and prediction methods through artificial intelligence algorithms are applied in different sectors to assist and promote intelligent decision-making. In this sense, due to the great importance in the cultivation, consumption and export of coffee in Brazil and the technological application of the Remotely Piloted Aircraft System (RPAS) this study aimed to compare and select models based on different data classification techniques by different classification algorithms for the prediction of different coffee cultivars (Coffea arabica L.) recently planted. The attributes evaluated were height, crown diameter, total chlorophyll content, chlorophyll A and chlorophyll B, Foliar Area Index (LAI) and vegetation indexes NDVI, NDRE, MCARI1, GVI, and CI in six months. The data were prepared programming language Python using algorithms of Decision Trees, Random Forest, Support Vector Machine and Neural Networks. It was evaluated through cross-validation in all methods, the distribution by FreeViz, the hit rate, sensitivity, specificity, F1 score, and area under the ROC curve and percentage and predictive performance difference. All algorithms showed good hits and predictions for coffee cultivars (0.768% Decision Tree, 0.836% Random Forest, 0.886 Support Vector Machine and 0.899 Neural Networks) and the Neural Networks algorithm produced more accurate predictions than other tested algorithm models, with a higher percentage of hits for the classes considered.