Segmentation of Green Vegetation from Crop Canopy Images Based on Fisher Linear Discriminant
One crucial problem with applying digital image analysis technology to the field of agriculture is separation of green vegetation regions. In this paper, a new crop canopy image segmentation algorithm, which is based on Fisher linear discriminant and the values of red, green and blue of each pixel in an image, is presented. First, 90 green vegetation regions and 80 non-green vegetation regions, each of which has nine pixels, from two crop canopy images were chosen to generate training data. Then, the optimal projection direction is determined by using the Fisher linear discriminant. Finally, a color crop canopy image is divided into two parts—green vegetation and non-green vegetation---with a fixed threshold. The algorithm’s performance was assessed on 50 images. The results on 50 images show that the median of mis-segmentation of proposed method is about 5%. The comparisons between the proposed algorithm and those based on color indices show that the former outperforms the latter with high segmentation rate and fast running speed.
L. Y. Zheng and K. Tian, "Segmentation of Green Vegetation from Crop Canopy Images Based on Fisher Linear Discriminant", Key Engineering Materials, Vol. 500, pp. 487-491, 2012