This title appears in the Scientific Report :
2020
Please use the identifier:
http://dx.doi.org/10.1007/978-3-030-65414-6_25 in citations.
Please use the identifier: http://hdl.handle.net/2128/26867 in citations.
Germination Detection of Seedlings in Soil: A System, Dataset and Challenge
Germination Detection of Seedlings in Soil: A System, Dataset and Challenge
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 tin...
Saved in:
Personal Name(s): | Scharr, Hanno (Corresponding author) |
---|---|
Bruns, Benjamin / Fischbach, Andreas / Roussel, Johanna / Scholtes, Lukas / vom Stein, Jonas | |
Contributing Institute: |
Pflanzenwissenschaften; IBG-2 |
Published in: |
Computer Vision – ECCV 2020 Workshops |
Imprint: |
Cambridge
Springer
2020
|
Physical Description: |
360 - 374 |
DOI: |
10.1007/978-3-030-65414-6_25 |
Conference: | 16th European Conference on Computer Vision, Glasgow, UK (UK), 2020-08-23 - 2020-08-28 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
Plant Science Innovative Synergisms |
Series Title: |
Lecture Notes in Computer Science
12540 |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/26867 in citations.
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. |