Automatic segmentation of relevant textures in agricultural images

Abstract

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.

Estimation of Fractional Vegetation Cover in Sand Dunes Using Multi-spectral Images from Fixed-wing UAV

“…Other advantages of UAV are their lower cost and great flexibility of configuration compared with piloted aircraft, as it allows the utilization and testing of lowcost sensors, such as conventional digital cameras. Some researchers estimated green vegetation covers using onground imagery taken with commercial cameras (Guijarro et al, 2011;Meyer and Neto, 2008;Romeo et al, 2013). In addition to their low cost, another advantage of conventional digital cameras is their high resolution, which is needed when working in narrow rows of vegetation, such as weeds.…”

Automatic expert system based on images for accuracy crop row detection in maize fields

“…Vegetation indices to be combined are computed as follows Guijarro et al (2011), Excess Green (Woebbecke et al, 1995;Ribeiro et al, 2005) Color index of vegetation extraction (Kataoka, Kaneko, Okamoto, & Hata, 2003) Vegetativen (Hague et al, 2006) Neto, 2004) where excess red is computed as follows (Meyer, Hindman, & Lakshmi 1998): ExR = 1.4r-g. According to Guijarro et al (2011) the above four indices are combined to obtain the resulting value COM as follows,…”

“…According to Guijarro et al (2011) the above four indices are combined to obtain the resulting value COM as follows,…”

“…soil surface, stones, shadows) as black. Also in Guijarro et al (2011) the well-known Otsu's (1979) method, traditionally applied for binarization, has been applied. More complex approaches have been also applied such as the one used in Bossu et al (2009), based on the fe-means clustering method.…”

“…According to point (a) in the list of knowledge and requirements above, the best option to identify weeds and crop is the application of vegetation indices instead of methods based on height discrimination. Vegetation indices are well tested methods, Guijarro et al (2011) propose a combination of vegetation indices, which is the one chosen in this paper because its performance in maize fields.…”