Anais da Academia Brasileira de Ciências
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/13096
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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.Item Influence of hulling and storage conditions on maintaining coffee quality(Academia Brasileira de Ciências, 2023-12-11) Abreu, Giselle F.; Rosa, Sttela D.V.F.; Coelho, Stefânia V.B.; Pereira, Cristiane C.; Malta, Marcelo R.; Fantazzini, Tatiana B.; Vilela, Amanda L.Storage is important in the coffee post-harvest. Determining the maximum period that coffee can remain storaged is important aiming to reduce losses in quality and, consequently, allow the producer to achieve maximum profitability. The aim was to determine the suitable storage period for natural and fully washed coffees, under different conditions. Beans were dried to 11% moisture content after dry processing (natural coffee, dry cherry coffee) and wet processing (parchment coffee, fully washed). Before storage, part of the coffee was hulled and part was not. The coffee was stored under refrigerated air (10ºC and 50% relative humidity) or in an environment at 25ºC. In the periods of 0, 3, 6, and 12 months, samples were taken for sensory, electrical conductivity and tetrazolium evaluation. Refrigerated environment favors conservation of sensory and physiological quality of the natural hulled coffee beans and fully washed coffee. Hulled beans of natural and fully washed coffee stored under refrigerated conditions have the initial quality conserved for up to 12 months and in non-controlled environmental, for up to 3 months. Mechanical damage caused by hulling, associated with the lack of tissue fruit parts, contributes to reduction hulled coffee quality in storage, regardless of the processing.