Navegando por Autor "Mincato, Ronaldo Luiz"
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Item Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee(Editora UFLA, 2020) Parreiras, Taya Cristo; Lense, Guilherme Henrique Expedito; Moreira, Rodrigo Santos; Santana, Derielsen Brandão; Mincato, Ronaldo LuizNitrogen is an essential element for coffee production. However, when fertilization do not consider the spatial variability of the agricultural parameters, it can generate economic losses, and environmental impacts. Thus, the monitoring of nitrogen is essential to the fertilizing management, and remote sensing based on unmanned aerial vehicles imagery has been evaluated for this task. This work aimed to analyze the potential of vegetation indices of the visible range, obtained with such vehicles, to monitor the nitrogen content of coffee plants in southern Minas Gerais, Brazil. Therefore, we performed leaf analysis using the Kjeldahl method, and we processed the images to produce the vegetation indices using Geographic Information Systems and photogrammetry software. Moreover, the images were classified using the Color Index of Vegetation and the Maximum Likelihood Classifier. As estimator tool, we created Random Forest models of classification and regression. We also evaluated the Pearson correlation coefficient between the nitrogen and the vegetation indices, and we performed the analysis of variance and the Tukey-Kramer test to assess whether there is a significant difference between the averages of these indices in relation to nitrogen levels. However, the models were not able to predict the nitrogen. The regression model obtained a R2 = 0.01. The classification model achieved an overall accuracy of 0.33 (33%), but it did not distinguish between the different levels of nitrogen. The correlation tests revealed that the vegetation indices are not correlated with the nitrogen, since the best index was the Green Leaf Index (R = 0.21). However, the image classification achieved a Kappa coefficient of 0.92, indicating that the tested index is efficient. Therefore, visible indices were not able to monitor the nitrogen in this case, but they should continue to be explored, since they could represent a less expensive alternative.Item Water erosion in oxisols under coffee cultivation(Sociedade Brasileira de Ciência do Solo, 2018) Mendes Júnior, Henrique; Tavares, André Silva; Santos, Walbert Júnior Reis dos; Silva, Marx Leandro Naves; Santos, Breno Régis; Mincato, Ronaldo LuizWater erosion is one of the main environmental impacts of land use. When soil and water losses occur, nutrients essential for the growth and maintenance of plants are removed, with harmful outcomes on the sustainability of agriculture and the environment. In addition, they lead to other deleterious effects, such as sedimentation and eutrophication of water bodies. Estimation of soil losses due to water erosion in sub-basins is essential for prediction of soil degradation, especially in areas of semi-intensive cultivation, such as coffee fields. Thus, the aim of this study was to estimate soil losses in relation to the limit of soil loss tolerance in Oxisols (Latossolos Vermelhos Distróficos) under coffee cultivation. This study was conducted from March 2015 to January 2017 in the Córrego da Laje Hydrographic Sub-basin in the municipality of Alfenas in the southern region of Minas Gerais, southeastern Brazil. Soil losses due to water erosion were estimated from the revised universal soil loss equation and compared to soil loss tolerance. Morphological, physical, and chemical properties of the soil were used, as well as geoprocessing techniques, remote-sensing images, and data from the literature. The results show potential soil losses from 0.01 to 18.77 Mg ha -1 yr -1 , with an average of 1.52 Mg ha -1 yr -1 . The soil loss tolerance ranged from 5.19 to 5.90 Mg ha -1 yr -1 , with 7.35 % of the area having larger losses. Areas with steeper slopes and no sustainable practices have soil losses above the tolerance level and are thus a priority for adoption of measures to mitigate erosive effects. The revised universal soil loss equation enabled water erosion modeling and identification of areas with the highest rates of potential soil loss in watersheds.