124test results obtain between 70.4 and 72.5% classification accuracy. Another work involves two processes: image segmentation and decision [2]. Source image is binarized applying a threshold from RGB format; with plant pixels, crop row direction is estimated; next, a Support Vector Machine classifier is trained and implemented to decide presence of weed offering an optimum performance in terms of computational cost.In [3], a work to identify weeds using an off-line training from RGB images of corn, these images are segmented to differentiate vegetation and soil, estimating a probability density function and partitioning the image into cells formed detecting from culture lines using Hough transform. In operation, the system calculates the cells of a new image and applies Bayes' theorem to make the decision process of belonging to the Weed class. The authors in [4] use a JAI AD-130 machine vision camera capable of simultaneously capturing visible and near-infrared light spectrums; they characterized soil, green and dry plants using difference between red grey level and near infrared grey level (index NIR-R) in crop images, achieving a clear threshold for separate and segment objects in the scene; the results indicate that 0 to 50 grey level for dry plants and 150 to 250 for green plants is a good way to classify vegetation, suggesting that plants and soil can be separated using a threshold from histogram. Another work about weed
Machine vision system for weed detection using image filtering in vegetables cropsABSTRACT: This work presents a machine vision system for weed detection in vegetable crops using outdoor images, avoiding lighting and sharpness problems during acquisition step. This development will be a module for a weed removal mobile robot with camera obscura (Latin for "dark room") for lighting controlled conditions. The purpose of this paper is to develop a useful algorithm to discriminate weed, using image filtering to extract color and area features, then, a process to label each object in the scene is implemented, finally, a classification based on area is proposed, including sensitivity, specificity, positive and negative predicted values in order to evaluate algorithm performance.
RESUMEN:El presente trabajo expone un sistema de visión de máquina para la detección de maleza en cultivos de hortalizas, usando imágenes exteriores, evadiendo problemas de iluminación y nitidez durante la etapa de adquisición, ya que el presente desarrollo será un módulo para un robot móvil removedor de maleza con una cámara oscura para controlar las condiciones de luz. El enfoque de diseño se enmarca en el desarrollo de un algoritmo útil para discriminar maleza usando filtros en la imagen extrayendo características de color y área, luego se implementa un proceso de etiquetado para cada objeto en la escena. Finalmente, una clasificación basada en área es propuesta incluyendo el cálculo de los índices de sensibilidad, especificidad, valores predictivos positivos y negativos con el fin de evaluar el rendimiento del algor...
