Abstract
Recently, several high-throughput phenotyping facilities have been established that allow for an automated collection of multiple view images of a large number of plants over time. One of the key problems in phenotyping is identifying individual plant organs such as leaves, stems, or roots. We introduced a novel algorithm that uses a 3D segmented plant on its input by using a voxel carving algorithm, and separates the plant into leaves and stems. Our algorithm first uses voxel thinning that generates a first approximation of the plant 3D skeleton. The skeleton is transformed into a mathematical tree by comparing and assessing paths from each leaf or stem tip to the plant root and pruned by using biologically inspired features, fed into a machine learning classifier, leading to a skeleton that corresponds to the input plant. The final skeleton is then used to identify the plant organs and segment voxels. We validated our system on 20 different plants, each represented in a voxel array of a resolution \(512^3\), and the segmentation was executed in under one minute, making our algorithm suitable for the processing of large amounts of plants.
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Acknowledgements
This research was supported by the Foundation for Food and Agriculture Research Grant ID: 602757 to Benes and Schnable. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the foundation for Food and Agriculture Research. This research was supported by the Office of Science (BER), U.S. Department of Energy, Grant no. DE-SC0020355 to Schnable. This work was supported by a National Science Foundation Award (OIA-1826781) to Schnable.
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Gaillard, M., Miao, C., Schnable, J., Benes, B. (2020). Sorghum Segmentation by Skeleton Extraction. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12540. Springer, Cham. https://doi.org/10.1007/978-3-030-65414-6_21
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