ABSTRACT Planting is considered one of the most essential steps in coffee growing. Lack of uniformity in planting may compromise future operations. Therefore, verifying planting operations quality is fundamental to optimizing production processes and reducing costs. This study aimed to investigate planting techniques through Statistical Process Control (SPC) and aerial images. Carried out in two areas, managed manually and semi-mechanized in the Bom Jardim Farm (MG - Brazil). Data were collected through Remotely Piloted Aircraft (RPA). Quality control charts and density maps were used to identify variations in distribution and spacing between plants and planting rows. It was found that the planting carried out manually was 4.7% wider than projected due to spacing reduction from 0.5 m to 0.48 m. The semi-mechanized system displayed a deficit of 7% compared to the projected planting system, using 0.55 m between plants. The density map showed the most significant planting alignment variations. Despite displaying lower results than the manual system, the semi-mechanized system improvements are valid for their minimal average variations. Thus, correcting points found outside the limits can increase the efficiency of semi-mechanized planting. growing Therefore costs SPC (SPC images areas semimechanized semi mechanized MG Brazil. Brazil . Brazil) RPA. RPA (RPA) rows 47 4 7 4.7 05 0 5 0. 048 48 0.4 055 55 Thus (RPA 4. 04

Resumo Vazões máximas e mínimas são fundamentais para a gestão dos recursos hídricos, especialmente para outorga de uso da água. Entretanto, a falta de monitoramento e a escassez de dados de vazão são fatores limitantes para tais estudos. Para contornar tal dificuldade, uma das ferramentas mais utilizadas é a regionalização de vazões. O objetivo neste estudo foi desenvolver modelos de regionalização para vazões máximas e mínimas de referênciapara a bacia hidrográfica do Rio das Mortes (MRB) (Unidade de Planejamento e Gestão dos Recursos Hídricos - GD2), sul do estado de Minas Gerais. As séries históricas de vazão de longo termo utilizadas foram disponibilizada pela Agência Nacional das Águas (ANA). Uma análise exploratório preliminar foi realizada, bem como a análise da estacionariedade das séries por meio do teste de tendências de Mann-Kendall. As Funções Densidade de Probabilidade (PDFs) com diferentes números de parâmetros foram utilizadas para estimar as vazões mínimas e máximas associadas a diferentes tempos de retorno e a performance foi avaliada por meio do teste de Anderson-Darling. A Generalized Extreme Values e Wakeby foram as distribuições mais apropriadas para vazões máximas e mínimas, respectivamente. O modelo de regressão para vazões foi ajustado utilizando como variável independente a área de drenagem das estações fluviométricas, sendo que os coeficientes de determinação obtidos foram superiores a 0,90. Desta forma, os modelos de regionalização demonstraram uma boa performance e são uma ferramenta potencial para ser utilizada na gestão dos recursos hídricos.

Abstract Maximum and minimum streamflow are fundamental for water resource management, especially for water rights. However, lack of monitoring and scarce streamflow data limit such studies. Streamflow regionalization is a useful tool to overcome these limitations. The study developed models for regionalization of the maximum and minimum reference streamflows for the Mortes River Basin (MRB) (Water Resources Planning and Management Unit - GD2), Southern Minas Gerais State. The study used long-term streamflow historical series provided by the Brazilian National Water Agency (ANA). Previous exploratory analysis was performed, and it was observed that the streamflow series are stationary according to the Mann-Kendall test. The estimation of the streamflow for different return periods (RP) was performed by fitting Probability Density Functions (PDFs) that were tested by the Anderson-Darling (AD) test. The Generalized Extreme Values (GEV) and Wakeby were the most appropriate PDFs for maximum and minimum streamflows, respectively. The streamflow models were fitted using a power regression procedure, considering the drainage area of the watersheds as inputs. The fittings reached the coefficient of determination (R2) greater than 0.90. Thus, the streamflow regionalization models demonstrated good performance and are a potential tool to be used for water resource management in the studied basin.