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.

This study aimed to modify a webcam by replacing its near-infrared (NIR) blocking filter to a low-cost red, green and blue (RGB) filter for obtaining NIR images and to evaluate its performance in two agricultural applications. First, the sensitivity of the webcam to differentiate normalized difference vegetation index (NDVI) levels through five nitrogen (N) doses applied to the Batatais grass (Paspalum notatum Flugge) was verified. Second, images from maize crops were processed using different vegetation indices, and thresholding methods with the aim of determining the best method for segmenting crop canopy from the soil. Results showed that the webcam sensor was capable of detecting the effect of N doses through different NDVI values at 7 and 21 days after N application. In the second application, the use of thresholding methods, such as Otsu, Manual, and Bayes when previously processed by vegetation indices showed satisfactory accuracy (up to 73.3%) in separating the crop canopy from the soil.

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