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

RESUMEN El desarrollo agrícola exige una mayor adopción de maquinaria por parte de los productores para evitar el daño a la salud de los trabajadores por el exceso de ruido. En este escenario, este estudio presenta análisis utilizando geoestadística y Control Estadístico de Procesos (SPC) en el mapeo de la variabilidad espacial de la magnitud del ruido emitido por un microtractor para identificar zonas saludables para los trabajadores. El estudio se llevó a cabo en la Universidad Federal de Lavras (UFLA), donde los niveles de ruido se recolectaron en puntos distribuidos en una cuadrícula de muestra regular de 2,0×2,0 m alrededor de la máquina. La dependencia espacial del ruido se analizó ajustando el semivariograma de tipo onda e interpolando por kriging ordinario y el SPC a través de cartas de control individuales. Fue posible observar valores de ruido superiores a 85 dB(A) en un radio de hasta 6 m alrededor del tractor en funcionamiento. El valor máximo de 91 dB(A) se obtuvo desde el asiento del operador, permitiendo así una exposición diaria máxima de 3,5 h. Además, se observó que las personas ubicadas a distancias mayores a 4 m del microtractor no necesitan usar equipo de protección personal para una exposición de 8 h de trabajo.

ABSTRACT Agricultural development requires greater adoption of machinery by producers to avoid damage to the worker's health due to excessive noise. In this scenario, this study aimed to analyze the noise magnitude emitted by a micro-tractor using geostatistics and Statistical Process Control (SPC) in mapping spatial variability to identify healthy zones for workers. The study was carried out at the Federal University of Lavras (UFLA), where noise levels were collected at points distributed in a regular 2.0×2.0 m sample grid around the machine. The spatial dependence of noise was analyzed by adjusting the wave-type semivariogram and interpolating by ordinary kriging and the SPC through individual control charts. It was possible to observe alarming noise values above 85 dB(A) in a radius of up to 6 m around the tractor in operation. The maximum value of 91 dB(A) was obtained from the operator's seat, thus allowing maximum daily exposure of 3.5 h. In addition, it was observed that people located at distances greater than 4 m from the micro-tractor do not need to wear personal protective equipment for an exposure of 8 h of work.

RESUMO O sensoriamento remoto multiespectral apresenta-se como metodologia confiável e viável para auxiliar o produtor na decisão para melhores práticas de manejo, garantindo uma produção agrícola mais eficiente e sustentável. Objetivou-se, com este trabalho, identificar e mapear o estresse em lavoura cafeeira, causado por variáveis bióticas e abióticas, por meio de índices de vegetação derivados de imagens multiespectrais Landsat-5 Thematic Mapper (TM). A malha amostral foi composta por 67 pontos, sendo cada ponto amostral constituído por cinco plantas. As análises de incidência de cercosporiose e de infestação do bicho-mineiro, nas folhas, de pH, matéria orgânica e textura do solo e teores foliares de nutrientes foram realizadas em cada um dos pontos amostrais e correlacionadas com 16 índices de vegetação obtidos de imagens referentes à época das análises. Os índices de vegetação apresentaram distribuição espacial semelhante à distribuição espacial das variáveis agronômicas, na lavoura. Houve correlação positiva dos índices com a infestação do bicho-mineiro e com os teores de silte e argila no solo e concentrações de Mg, Cu, B e Mn nas folhas, e negativa, com a incidência de cercosporiose e com pH e teor de areia do solo. Com base nesses resultados, foi possível mapear e identificar as alterações na reflectância espectral dos cafeeiros, causadas por essas variáveis agronômicas.

ABSTRACT Multispectral remote sensing is a reliable and feasible methodology to assist farmers in decision making for best management practices, ensuring a more efficient and sustainable agricultural production. The objective of this study was to identify and map stress on coffee caused by biotic and abiotic variables through vegetation indices derived from Landsat-5 Thematic Mapper (TM) multispectral images. The sampling grid was composed of 67 points, with each sampling point consisting of five plants. The analyzes of the incidence of brown eye spot and infestation of the leaf miner in the leaves, pH, organic matter, soil texture and nutrients leaf contents were performed at each of the sampling points and correlated with 16 vegetation indices obtained from images at the time of analysis. The vegetation indices presented a spatial distribution similar to the agronomic variables in the crop. There was a positive correlation of the indices with infestation of the leaf miner, silt and clay content in the soil and concentration of Mg, Cu, B and Mn in the leaves, and negative with the incidence of brown eye spot, pH and soil sand content. Based on these results, it was possible to map and identify the changes in the spectral reflectance of the coffee trees, caused by these agronomic variables.