SEGMENTAÇÃO DE IMAGENS RGB USANDO DIFERENTES ÍNDICES DE VEGETAÇÃO E MÉTODOS DE LIMIARIZAÇÃO A segmentação é um dos aspectos fundamentais envolvidos no processamento de imagens, que geralmente consiste na discriminação de objetos de interesse e fundo da imagem. O presente estudo objetivou avaliar o efeito de diferentes índices de vegetação (IV) (ExG, ExGR e NDI) no desempenho de três métodos de limiarização (Otsu, Ridler e Triângulo) em termos de precisão e tempo de processamento na segmentação de imagens. Para tal, foram utilizadas 30 imagens advindas de área cultivada com milho sob diferentes tipos de cobertura do solo (plantio convencional, casca de café e palhada). O processamento das imagens foi realizado através de algoritmos desenvolvidos com base nos IV e métodos de limiarização. A acurácia das imagens resultantes foi avaliada com a verdade de campo obtida pelo algoritmo K-means. Os resultados demonstraram desempenho superior para o método do triângulo quando precedido dos índices NDI (90,7%) e ExGR (90,23%) e dos métodos de Otsu e Ridler quando precedidos pelo NDI com 89,06% e 89,03% de acurácia, respectivamente. O tempo de processamento foi estatisticamente igual entre os métodos avaliados. De modo geral, a abordagem combinada de IV e métodos de limiarização foram capazes de separar com alta acurácia a cultura do milho do objeto de fundo. Palavras-chave: processamento de imagens, imagens digitais, método do triângulo. ABSTRACT: Image Segmentation is one of the fundamental aspects involved in image processing, which generally consists of discriminating objects of interest from its background. Thus, the objective of this study was to evaluate the effect of vegetation indices (VI) (ExG, ExGR, and NDI) on the performance of three automated thresholding methods (Otsu, Ridler, and Triangle) in terms of accuracy and processing time on image segmentation. A set of 30 images from an area cultivated with maize under different types of soil cover (conventional planting, no-tillage with coffee husk, and straw residue) were selected and processed. The images were processed through algorithms developed based on VI and thresholding methods. Then, the accuracy of the resulting images was evaluated through the ground truth image obtained by the K-means algorithm. The results demonstrated superior performance for the triangle method when preceded by the NDI (90.7%) and ExGR (90.23%) indices and the Otsu and Ridler methods when preceded by the NDI with 89.06% and 89.03% accuracy, respectively. The processing time was statistically equal among the evaluated methods. In general, the combined approach of VI and thresholding based methods were capable of separating with high accuracy the maize crop from the background. Keywords: image processing, digital images, triangle method.

The increasing market interest in coffee beverage, lead coffee growers around the world to adopt more efficient methods to select the best-quality coffee beans. Currently, coffee beans selection is carried out either manually, which is a costly and unreliable process, or using electronic sorting machines, which are often inefficient because some coffee beans defects, such as sour and immature beans, have similar spectral response patterns. In this sense, the present work aimed to assess coffee beans quality using both computer vision and machine learning techniques, such as Support Vector Machine (SVM), Deep Neural Network (DNN) and Random Forest (RF). For this purpose, an algorithm written in Python language was developed to extract shape and color features from coffee beans images. The obtained dataset was then used as input to the machine learning algorithms. The data reported in this study pointed to the importance of color descriptors for classifying coffee beans defects. Among the variables used, the components from RGB (Red, Green and Blue) and HSV (Hue, Saturation and Value) color spaces presented the most relevant contribution for the classification models. Also, the results reported in this study provides evidence that computer vision along with machine learning algorithms can be used to identify and classify coffee beans with a very high accuracy (> 90%). Key words: Deep neural network; classification; artificial intelligence; image processing; granulometry.

Spatial interpolation methods are frequently used to characterize soil attributes’ spatial variability. However, inconclusive results, about the comparative performance of these methods, have been reported in the literature. Therefore, the present study aimed to analyze the efficiency of ordinary kriging (OK) and inverse distance weighting (IDW) methods in estimating the soil penetration resistance (SPR), soil bulk density (SBD), and soil moisture content (SM) using two distinct sampling grids. The soil sampling was performed on a 5.7 ha area in Southeast Brazil. For data collection, a regular grid with 145 points (20 x 20 m) was created. Soil samples were taken at a 0.20 m layer depth. In order to compare the accuracy of OK and IDW, another grid was created from the initial grid (A), by eliminating one interspersed line, which resulted in a grid with 41 sampled points (40 x 40 m). Results showed that sampling grid A presented less errors than B, proving that the more sampling points, the lower the errors that are associated with both methods will be. Overall, the OK was less biased than IDW only for SBD (A) and SM (B) maps, whereas IDW outperformed OK for the other attributes for both sampling grids.

Utility All-Terrain Vehicles (ATVs) are one major cause of youth injuries and fatalities on farms. Utility ATVs have heavy weights and fast speeds that require complex maneuvering. Youth's physical capabilities may not be sufficient to perform those complex maneuvers correctly. Therefore, it is hypothesized that most youth engage in ATV-related incidents because they ride vehicles unfit for them. There is a need to assess ATV-youth fit based on youth anthropometry. This study focused on evaluating potential inconsistencies between the operational requirements of utility ATVs and the anthropometric measures of youth through virtual simulations. Virtual simulations were performed to assess 11 youth-ATV fit guidelines proposed by several ATV safety advocacy organizations (National 4-H council, CPSC, IPCH, and FReSH). In total, 17 utility ATVs along with male-and-female-youth of nine ages (8 to 16 years old) and three height percentiles (5th, 50th, and 95th) were evaluated. The results demonstrated a physical mismatch between ATVs' operational requirements and youth's anthropometry. For example, male-youth aged 16 of the 95th height percentile failed to pass at least 1 out of the 11 fit guidelines for 35 % of all vehicles evaluated. The results were even more concerning for females. Female youth 10 years old and younger (from all height percentiles) failed to pass at least one fit guideline for all ATVs evaluated. Youth are not recommended to ride utility ATVs. This study provides quantitative and systematic evidence to modify current ATV safety guidelines. Furthermore, youth occupational health professionals could use the present findings to prevent ATV-related incidents in agricultural settings.