Abstract
Aims
Roots are vital organs for plants, but the assessment of root traits is difficult, particularly in deep soil layers under natural field conditions. A popular technique to investigate root growth under field or semi-field conditions is the use of minirhizotrons. However, the subsequent manual quantification process is time-consuming and prone to error.
Methods
We developed a multispectral minirhizotron imaging system and a subsequent image analysis strategy for automated root detection. Five wavelengths in the visible (VIS) and near-infrared (NIR) spectrum are used to enhance living roots by a multivariate grou** of pixels based on differences in reflectance; background noise is suppressed by a vesselness enhancement filter. The system was tested against manual analysis of grid intersections for both spring barley (Hordeum vulgare L.) and perennial ryegrass (Lolium perenne L.) cultivars at two time-points. The images of living roots were captured in wet subsoil conditions with dead roots present from a previous crop.
Results
Under the soil conditions used in the study, NIR reflectance (940 nm), provided limited ability to separate between rhizosphere components, compared to reflectance in the violet and blue light spectrum (405 nm and 450 nm). Multivariate image analysis of the spectral data, combined with vesselness enhancement and thresholding allowed for automated detection of living roots. Automated image analysis largely replicated the root intensity found during manual grid intersect analysis of the same images. Although some misclassification occurred, caused by elongated structures of dew and chalkstone with similar reflectance pattern as living root, the system provided similar or in some cases improved detection of genotypic differences in the total root length within each tube.
Conclusion
The multispectral imaging system allows for automated detection of living roots in minirhizotron studies. The system requires considerably less time than traditional manual recording using grid intersections. The flexible training strategy used for root segmentation offers hope for the transfer to other rhizosphere components and other soil types of interest.
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Abbreviations
- ANCOVA:
-
Analysis of covariance
- ANOVA:
-
Analysis of variance
- AI:
-
Automated image analysis
- M:
-
Manual grid intersections
- MR:
-
Minirhizotron
- NIR:
-
Near-infrared
- nCDA:
-
normalized Canonical Discriminant Analysis
- BBCH:
-
Scale to identify the phonological development stages of plants
- VIS:
-
Visible
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Acknowledgements
We would like to acknowledge Jon Nielsen of the Department of Plant and Environmental Science for MatLab script development and Bo Markussen from Data Science Lab for assistance with the statistical analysis, both University of Copenhagen. Furthermore, we would like to thank the technical efforts of many colleagues for valuable discussions of root quantifications in soil and assistance of the manually grid intersection procedure. Furthermore, the contributions of plant material and technical assistance from plant breeders at Sejet, Nordic Seed, DLF Trifolium and Danespo are also gratefully acknowledged. The project was funded by the Innovation Fund Denmark (Grant No. 46-2014-1), Crop Innovation Denmark, Promilleafgiftsfonden and PlanDanmark.
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S.F.S and K.T.K designed the field experiment. S.F.S collected and analyzed the data and drafted the first manuscript. All authors contributed to improving the manuscript. JMC and coworkers developed the multispectral camera and the multivariate image analysis. EBD performed the initial Frangi filter calibration and developed the final segmentation of root structures by thresholding and skeletonization. All authors reviewed and approved the final manuscript.
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J.M.C is the founder of the company Videometer A/S. Videometer A/S developed the multispectral camera system and the VideometerLab software used to perform the first part of the image analysis in this paper. This may lead to a conflict of interest in the use of multispectral imaging for root segmentation. It should be noted that both the camera and LEDs used are available from other providers. Furthermore, the multivariate image analysis can be performed in a broad range of other software solutions such as MatLab or GNU Octave. All other authors declare no conflict of interest.
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Svane, S.F., Dam, E.B., Carstensen, J.M. et al. A multispectral camera system for automated minirhizotron image analysis. Plant Soil 441, 657–672 (2019). https://doi.org/10.1007/s11104-019-04132-8
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DOI: https://doi.org/10.1007/s11104-019-04132-8