A fast and efficient method for solving the multiple line detection problem

In this paper, we consider the multiple line detection problem on the basis of a data points set coming from a number of lines not known in advance. A new and efficient method is proposed, which is based upon center-based clustering, and it solves this problem quickly and precisely. The method has been tested on 100 randomly generated data sets. In comparison to the incremental algorithm, the method gives significantly better results. Also, in order to identify a partition with the most appropriate number of clusters, a new index has been proposed for the case of a cluster whose lines are cluster-centers. The index can also be generalized for other geometrical objects

Real-time image processing for crop/weed discrimination in maize fields

This paper presents a computer vision system that successfully discriminates between weed patches and crop rows under uncontrolled lighting in real-time. The system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem's mistakes. This combination produces a system that achieves very good results under a wide variety of conditions. Tested on several maize videos taken of different fields and during different years, the system successfully detects an average of 95% of weeds and 80% of crops under different illumination, soil humidity and weed/crop growth conditions. Moreover, the system has been shown to produce acceptable results even under very difficult conditions, such as in the presence of dramatic sowing errors or abrupt camera movements. The computer vision system has been developed for integration into a treatment system because the ideal setup for any weed sprayer system would include a tool that could provide information on the weeds and crops present at each point in real-time, while the tractor mounting the spraying bar is movin

Image-Based Particle Filtering For Robot Navigation In A Maize Field

Autonomous navigation of a robot in an agricultural field is a challenge as the robot is in an environment with many sources of noise. This includes noise due to uneven terrain, varying shapes, sizes and colors of the plants, imprecise sensor measurements and effects due to wheel-slippage. The drawback of current navigation systems in use in agriculture is the lack of robustness against such noise. In this study we present a robust vision-based navigation method based on probabilistic methods. The focus is on navigation through a corn field. Here the robot has to navigate along the rows of the crops, detect the end of the rows, navigate in the headland and return in another row. A Particle Filter based navigation method is used based on a novel measurement model. This model results in an image from the particle state vector that allows the user to compare the observed image with the actual field conditions. In this way the noise is incorporated into the posterior distribution of the particle filter. The study shows that the new method accurately estimates the robot-environment state by means of a field experiment in which the robot navigates through the field using the particle filter

Line Cluster Detection Using A Variant Of The Hough Transform For Culture Row Localisation

An adaptation of the Hough transform was proposed for the detection of line clusters of known geometry. This method was applied in agriculture for the detection of sowing furrows created by a driller and of chicory plant rows during harvesting process. The sowing rows were revealed by a background correction, the background being obtained thanks to a median rank filter. The method was found efficient in eliminating the shadows. For the crop rows, a neural network was used to localise the plants. While the petiole and the leaves were easily separated from the soil, the chicory root and the soil having about the same colour and the lighting condition varying widely, it was more difficult to obtain a good contrast between those parts, which leaves place for some improvements. The adapted Hough transform consisted in computing one transform for each line in the cluster with, for reference, the position and direction of the theoretical position of the row. The different transforms were then added. It was found effective for both the sowing rows and the chicory rows. Results remained good even in very noisy conditions, when the rows were incomplete or when artefacts would lead its classical counter part to show several alignments other than the expected ones. The culture rows were localised with a precision of a few centimetres which was compatible with the proposed applications.RECOTRAC

Métodos de visión por computador para detección automática de líneas de cultivo curvas/rectas y malas hierbas en campos de maíz

Cada día es mayor el uso de sistemas de visión por computador a bordo de vehículos autónomos para agricultura de precisión y su utilización en distintas tareas, demandando una atención especial. La discriminación entre cultivo y malas hierbas así como la identificación de las líneas de cultivo en imágenes obtenidas en campos de maíz (cultivo de surcos anchos) representan importantes retos, tanto desde el punto de vista de la aplicación de tratamientos selectivos como para un guiado preciso en la navegación de los mencionados vehículos. En cualquier caso, la calidad de las imágenes se ve afectada por las condiciones de iluminación no controladas en entornos agrícolas de exterior. Además, diferentes alturas y volúmenes de las plantas que se manifiestan por los distintos estados de crecimiento y la presencia de discontinuidades a lo largo de las líneas de cultivo debido a una mala germinación o defectos durante la siembra, dificultan los procesos de detección de líneas de cultivo y discriminación entre cultivo y malas hierbas. Las imágenes fueron tomadas bajo proyección de perspectiva con una cámara instalada a bordo del tractor y convenientemente colocada en la parte frontal. Con respecto a la detección de las líneas de cultivo, se han propuesto dos nuevos métodos para la detección de líneas curvas y rectas en campos de maíz durante los estados iniciales de crecimiento del cultivo y malas hierbas. El objetivo final es la identificación de las líneas de cultivo con dos propósitos: a) guiado preciso en vehículos autónomos; b) tratamientos específicos, incluyendo la eliminación de malas hierbas, situadas entre las líneas. Los métodos propuestos se diseñaron con la robustez requerida para abordar el problema de las condiciones adversas indicadas previamente y constan de tres fases consecutivas: (i) segmentación de la imagen, (ii) identificación de los puntos de comienzo de las líneas de cultivo y (iii) detección de las propias líneas. La principal contribución de estos métodos estriba en su capacidad para detectar líneas de cultivo curvas y rectas con espaciados regulares o irregulares entre las líneas, incluso cuando coexisten tipos de líneas en el mismo campo e imagen. Ambos métodos, difieren entre ellos en la fase de detección. Uno se basa en la acumulación de píxeles verdes y el otro en lo que se conoce como concepto de micro-ROIs (Region Of Interest). Los rendimientos de los métodos propuestos se compararon cuantitativamente frente a cinco estrategias existentes, consiguiendo precisiones entre el 86.3% y el 92.8%, dependiendo de si las líneas de cultivo son curvas o rectas con espaciado regular o irregular, con tiempos de procesamiento menores que 0.64 s por imagen..