Germination Detection of Seedlings in Soil: A System, Dataset and Challenge

Scharr, Hanno; Roussel, Johanna; Scholtes, Lukas; Fischbach, Andreas; vom Stein, Jonas; Bruns, Benjamin

doi:10.1007/978-3-030-65414-6_25

Contribution to a conference proceedings/Contribution to a book

FZJ-2021-00207

Germination Detection of Seedlings in Soil: A System, Dataset and Challenge

Scharr, H. (Corresponding author)FZJ* ; Bruns, B.FZJ* ; Fischbach, A.FZJ* ; Roussel, J. ; Scholtes, L.FZJ* ; vom Stein, J.FZJ*

2020
Springer Cambridge

Computer Vision – ECCV 2020 Workshops
16th European Conference on Computer Vision, ECCV 2020, Glasgow, UK, UK, 23 Aug 2020 - 28 Aug 2020 Cambridge : Springer, Lecture Notes in Computer Science 12540, 360 - 374 (2020) [10.1007/978-3-030-65414-6_25]

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Please use a persistent id in citations: http://hdl.handle.net/2128/26867 doi:10.1007/978-3-030-65414-6_25

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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