This title appears in the Scientific Report :
2022
Please use the identifier:
http://dx.doi.org/10.34133/2022/9758532 in citations.
Please use the identifier: http://hdl.handle.net/2128/32489 in citations.
Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline
Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline
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...
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Personal Name(s): | Bauer, Felix Maximilian (Corresponding author) |
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Lärm, Lena / Morandage, Shehan / Lobet, Guillaume / Vanderborght, Jan / Vereecken, Harry / Schnepf, Andrea | |
Contributing Institute: |
Agrosphäre; IBG-3 |
Published in: | Plant phenomics, 2022 (2022) S. 1 - 14 |
Imprint: |
Washington, D.C.
American Association for the Advancement of Science
2022
|
DOI: |
10.34133/2022/9758532 |
Document Type: |
Journal Article |
Research Program: |
EXC 2070: PhenoRob - Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion Agro-biogeosystems: controls, feedbacks and impact |
Link: |
Get full text OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/32489 in citations.
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. |