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| 001 | 911250 | ||
| 005 | 20230815122844.0 | ||
| 024 | 7 | _ | |a 10.34133/2022/9758532 |2 doi |
| 024 | 7 | _ | |a 2128/32489 |2 Handle |
| 024 | 7 | _ | |a 35693120 |2 pmid |
| 024 | 7 | _ | |a WOS:000834077000003 |2 WOS |
| 037 | _ | _ | |a FZJ-2022-04546 |
| 082 | _ | _ | |a 580 |
| 100 | 1 | _ | |a Bauer, Felix Maximilian |0 P:(DE-Juel1)186730 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline |
| 260 | _ | _ | |a Washington, D.C. |c 2022 |b American Association for the Advancement of Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1671710669_15813 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using “RootPainter.” Then, an automated feature extraction from the segments is carried out by “RhizoVision Explorer.” To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation (r=0.9) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches. |
| 536 | _ | _ | |a 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) |0 G:(DE-HGF)POF4-2173 |c POF4-217 |x 0 |f POF IV |
| 536 | _ | _ | |a DFG project 390732324 - EXC 2070: PhenoRob - Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion |0 G:(GEPRIS)390732324 |c 390732324 |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Lärm, Lena |0 P:(DE-Juel1)180553 |b 1 |
| 700 | 1 | _ | |a Morandage, Shehan |0 P:(DE-Juel1)168106 |b 2 |
| 700 | 1 | _ | |a Lobet, Guillaume |0 P:(DE-Juel1)171180 |b 3 |
| 700 | 1 | _ | |a Vanderborght, Jan |0 P:(DE-Juel1)129548 |b 4 |
| 700 | 1 | _ | |a Vereecken, Harry |0 P:(DE-Juel1)129549 |b 5 |
| 700 | 1 | _ | |a Schnepf, Andrea |0 P:(DE-Juel1)157922 |b 6 |
| 773 | _ | _ | |a 10.34133/2022/9758532 |g Vol. 2022, p. 1 - 14 |0 PERI:(DE-600)2968615-5 |p 1 - 14 |t Plant phenomics |v 2022 |y 2022 |x 2643-6515 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/911250/files/Invoice_APC600314256.pdf |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/911250/files/9758532.pdf |y OpenAccess |
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| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Erde und Umwelt |l Erde im Wandel – Unsere Zukunft nachhaltig gestalten |1 G:(DE-HGF)POF4-210 |0 G:(DE-HGF)POF4-217 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-200 |4 G:(DE-HGF)POF |v Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten |9 G:(DE-HGF)POF4-2173 |x 0 |
| 914 | 1 | _ | |y 2022 |
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