Greenness identification based on HSV decision tree

Abstract

Greenness identification from crop images captured outdoors is the important step for crop growth monitoring. The commonly used methods for greenness identification are based on visible spectral-index, such as the excess green index, the excess green minus excess red index, the vegetative index, the color index of vegetation extraction, the combined index. All these visible spectral-index based methods are working on the assumption that plants display a clear high degree of greenness, and soil is the only background element. In fact, the brightness and contrast of an image coming from outdoor environments are seriously affected by the weather conditions and the capture time. The color of the plant varies from dark green to bright green. The back ground elements may contain crop straw, straw ash besides soil. These environmental factors always make the visible spectral-index based methods unable to work correctly. In this paper, an HSV decision tree based method for greenness identification from maize seedling images captured outdoors is proposed. Firstly, the image was converted from RGB color space to HSV color space to avoid influence of illumination. Secondly, most of the background pixels were removed according to their hue values compared with the ones of green plants. Thirdly, the pixels of wheat straws whose hue values were intersected with tender green leaves were eliminated subject to their hues, saturations and values. At last, thresholding was employed to get the green plants. The results indicate that the proposed method can recognize greenness pixels correctly from the crop images captured outdoors.