SEGMENTATION OF RGB IMAGES USING DIFFERENT VEGETATION INDICES AND THRESHOLDING METHODS

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

SEGMENTATION OF RGB IMAGES USING DIFFERENT VEGETATION INDICES AND THRESHOLDING METHODS

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

Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks

The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R2 = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision

Forage Mass Estimation in Silvopastoral and Full Sun Systems: Evaluation through Proximal Remote Sensing Applied to the SAFER Model

The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method