Abstract
Generally, plant grows slowly and is difficult to be observed, the apical growing points can reflect the changes of plant, such that the extraction of apical growing points is helpful for the analysis of plant growth. In this paper, a new digital visual-based method of tomato apical growing points segmentation is proposed, which is depended on depth segmentation, color segmentation and position histogram statistic. First of all, use the depth image captured by KinectV2 to remove complex background through depth segmentation. Then, position histogram of the two value image after depth segmentation has been obtained to get the column position of the apical growing points. Using the KinectV2 coordinate mapping mechanism to restore the color information of the two value image, and then the RBG-D image can be color segmented. Finally, the region of the apical growing points is segmented by coordinate mapping, and the apical growing point is extracted by the contour detection. The experimental results show that the method to segment the growth environment is effective.
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Liu, P., Li, X., Zhou, Q. (2017). Apical Growing Points Segmentation by Using RGB-D Data. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_58
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DOI: https://doi.org/10.1007/978-981-10-6370-1_58
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