Adaptive segmentation of plant images, an integration of color space features and self-organizing maps

We developed an adaptive learning for segmentation of plant images into plant and non-plant regions. In this study, we used Kohonen's self organizing map (SOM) algorithm for segmentation of plant images using image series of two complexity levels; images taken in a controlled environment of plant facility and also images taken in the field. Nine color features of three color models of normalized Red Green Blue (RGB), Hue Saturation and Intensity (HSI) and L*a*b* made up the feature map. The results showed good performance for the images with less complexity. However, for images with higher complexity where there are more regions with similar color pattern, the method produces some noise.