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000889315 0247_ $$2doi$$a10.1007/978-3-030-65414-6_25
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000889315 037__ $$aFZJ-2021-00207
000889315 041__ $$aEnglish
000889315 1001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b0$$eCorresponding author$$ufzj
000889315 1112_ $$a16th European Conference on Computer Vision$$cGlasgow, UK$$d2020-08-23 - 2020-08-28$$gECCV 2020$$wUK
000889315 245__ $$aGermination Detection of Seedlings in Soil: A System, Dataset and Challenge
000889315 260__ $$aCambridge$$bSpringer$$c2020
000889315 29510 $$aComputer Vision – ECCV 2020 Workshops
000889315 300__ $$a360 - 374
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000889315 4900_ $$aLecture Notes in Computer Science$$v12540
000889315 520__ $$aIn phenotyping experiments plants are often germinated in high numbers, and in a manual transplantation step selected and moved to single pots. Selection is based on visually derived germination date, visual size, or health inspection. Such values are often inaccurate, as evaluating thousands of tiny seedlings is tiring. We address these issues by quantifying germination detection with an automated, imaging-based device, and by a visual support system for inspection and transplantation. While this is a great help and reduces the need for visual inspection, accuracy of seedling detection is not yet sufficient to allow skipping the inspection step. We therefore present a new dataset and challenge containing 19.5k images taken by our germination detection system and manually verified labels. We describe in detail the involved automated system and handling setup. As baseline we report the performances of the currently applied color-segmentation based algorithm and of five transfer-learned deep neural networks.
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000889315 7001_ $$0P:(DE-Juel1)128666$$aBruns, Benjamin$$b1$$ufzj
000889315 7001_ $$0P:(DE-Juel1)129315$$aFischbach, Andreas$$b2$$ufzj
000889315 7001_ $$0P:(DE-Juel1)129392$$aRoussel, Johanna$$b3
000889315 7001_ $$0P:(DE-Juel1)173690$$aScholtes, Lukas$$b4$$ufzj
000889315 7001_ $$0P:(DE-Juel1)167520$$avom Stein, Jonas$$b5$$ufzj
000889315 773__ $$a10.1007/978-3-030-65414-6_25
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