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A method was constructed to extract individual plant image from the continuously-collected images. The leaf surface region segmentation was obtained, and the individual plant image was separated based on leaf surface image by leaf distance. The two consecutive adjacent images were stitched together, and the newly-formed images were transformed and stitched with empty images to produce individual plant image for determining the centroid. The distance between the centroid of all stitched images in the previous image and the centroid of all the stitched images in the subsequent image was compared. The distance threshold method was used to identify whether the two images were the images of the same crop. Transfer closure was applied for obtaining the plant image set of each crop in multiple continuous images, and the image closest to the center was selected as the best image. Five crops were used to verify the proposed method. The results show that the accuracy of plant number reaches 100%. The proposed method can successfully and precisely extract individual plant image with high computational efficiency. [ABSTRACT FROM AUTHOR]

Leaf surface region segmentation from crop images is a crucial step for obtaining crop information. The leaf surface region segmentation （LSRS）from crop images was proposed. The color segmentation was used to obtain green region in crop images， and the smoothness segmentation was applied to identify smooth parts in green regions. Using the erosion method and the pixel number of each region， the noises were eliminated by LSRS to separate big noise and leaf regions into different parts by connectivity scheme. The dilation method was finally used to obtain whole leaf surface region. The experiments were conducted with several crop leaf images to verify the proposed method.The results show that compared to existing methods, the proposed system can automatically segment leaf surface region from complicated crop images. [ABSTRACT FROM AUTHOR]