Abstract
In phenotyping experiments plants are often germinated in high numbers, and in a manual transplantation step selected and moved to single pots. Selection is based on visually derived germination date, visual size, or health inspection. Such values are often inaccurate, as evaluating thousands of tiny seedlings is tiring. We address these issues by quantifying germination detection with an automated, imaging-based device, and by a visual support system for inspection and transplantation. While this is a great help and reduces the need for visual inspection, accuracy of seedling detection is not yet sufficient to allow skipping the inspection step. We therefore present a new dataset and challenge containing 19.5k images taken by our germination detection system and manually verified labels. We describe in detail the involved automated system and handling setup. As baseline we report the performances of the currently applied color-segmentation based algorithm and of five transfer-learned deep neural networks.
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Notes
- 1.
For definitions of accuracy, precision and recall, please see Section B in the supplemental material.
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Acknowledgements
Part of this work has been supported by the network for phenotyping science: CROP.SENSe.net, funded by German BMBF (0315531C). The authors thank Silvia Braun, Thorsten Brehm, and Birgit Bleise for testing the system in their practical work and giving feedback for improvements.
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Scharr, H., Bruns, B., Fischbach, A., Roussel, J., Scholtes, L., Stein, J.v. (2020). Germination Detection of Seedlings in Soil: A System, Dataset and Challenge. 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_25
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