Keywords:Machine vision Image segmentation Texture identification in crops Automatic tasks in agriculture One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing.

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This paper describes a new automatic image segmentation strategy for segmenting green plants. The final goal is its application in Precision Agriculture. The goal is to identify several classes of greenness coming from the plants. We exploit the performance of several existing approaches so that conveniently combined allow us to design the automatic approach based on non automatic methods. First we apply a well known index-based strategy that accentuates the green spectral band from the remainder, giving a gray image. From the resulting image we apply the well-known thresholding Otsu's method obtaining a binary image, where the green part appears separated from the soil. Taking as input the green pixels we apply an unsupervised method and they are partitioned in a fixed number of classes. The performance of the method is tested against a set of available images and acquired in several crop fields of cereal and maize.