Uptake of subsoil water below 2 m fails to alleviate drought response in deep-rooted Chicory (Cichorium intybus L.)

Rasmussen, Camilla Ruø; Thorup‐Kristensen, Kristian; Dresbøll, Dorte Bodin

doi:10.1007/s11104-019-04349-7

Cited by 34 publications

(29 citation statements)

References 63 publications

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“…1). The images had been used in a previous study [51] where the analysis was performed using the manual line-intersect method. As we make no modifications to the hardware or photographic procedure, we are able to evaluate our method as a drop-in replacement to the manual line-intersect method.…”

Section: Methodsmentioning

confidence: 99%

“…There were a total of 3300 images which had been taken on 9 different dates during 2016. The photos were taken from depths between 0.3 and 4 m. Four photos were taken of each panel in order to cover its full width, with each individual image covering the full height and 1/4 of the width (For further details of the experiment and the facility see [51]). The image files were labelled according to the specific rhizotron, direction and panel they are taken from with the shallowest which is assigned the number 1 and the deepest panel being assigned the number 20.…”

Section: Methodsmentioning

confidence: 99%

“…To facilitate comparison with the available root intensity measurements by analysing the same region of the image as [51], the images were cropped from their original dimensions of 4608 × 2592 pixels to 3991 × 1842 pixels which corresponds to an area of approximately 300 × 170 mm of the surface of the rhizotron. This was done by removing the right side of the image where an overlap between images is often present and the top and bottom which included the metal frame around the acrylic glass.…”

Section: Annotationmentioning

confidence: 99%

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Segmentation of roots in soil with U-Net

et al. 2020

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Background Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods. Rhizotrons allow visual inspection of root growth through transparent surfaces. Agronomists currently manually label photographs of roots obtained from rhizotrons using a line-intersect method to obtain root length density and rooting depth measurements which are essential for their experiments. We investigate the effectiveness of an automated image segmentation method based on the U-Net Convolutional Neural Network (CNN) architecture to enable such measurements. We design a data-set of 50 annotated chicory (Cichorium intybus L.) root images which we use to train, validate and test the system and compare against a baseline built using the Frangi vesselness filter. We obtain metrics using manual annotations and line-intersect counts. Results Our results on the held out data show our proposed automated segmentation system to be a viable solution for detecting and quantifying roots. We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0.9748 and an $$r^2$$r2 of 0.9217. We also achieve an $$F_1$$F1 of 0.7 when comparing the automated segmentation to the manual annotations, with our automated segmentation system producing segmentations with higher quality than the manual annotations for large portions of the image. Conclusion We have demonstrated the feasibility of a U-Net based CNN system for segmenting images of roots in soil and for replacing the manual line-intersect method. The success of our approach is also a demonstration of the feasibility of deep learning in practice for small research groups needing to create their own custom labelled dataset from scratch.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Annotationmentioning

confidence: 99%

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Segmentation of roots in soil with U-Net

et al. 2020

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“…Drip irrigation with a controlled water flow of 15 mm hour −1 was installed in each chamber. For a thorough description of the facility, see Rasmussen et al (2019).…”

Section: Main Experimentsmentioning

confidence: 99%

The effect of drought and intercropping on chicory nutrient uptake from below 2 m studied in a multiple tracer setup

2019

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Aims We tested if chicory acquires nutrients from soil layers down to 3.5 m depth and whether the deep nutrient uptake increases as a result of drought or intercropping with ryegrass and black medic. We also tested whether application of the trace elements Cs, Li, Rb, Sr and Se, as tracers, result in similar uptake rates. Methods The methodological tests were carried out in a pilot experiment where the tracers were applied to 1 m depth in lucerne and red beet grown in tube rhizotrons. The dynamics of deep nutrient uptake in chicory was studied in large 4 m deep rhizoboxes. A drought was induced when roots had reached around 2 m depth. Results Chicory acquired 15 N from 3.5 m depth and trace element tracers from 2.3 m depth. We found no compensatory tracer uptake with depth during drought. We found some indications of a compensatory tracer uptake from 2.3 and 2.9 m depth in intercropped chicory. Application of equimolar amounts of trace elements resulted in similar excess tracer concentrations within species. Conclusion Chicory demonstrates nutrient uptake from below 3 m but does not increase deep nutrient uptake as a response to limited topsoil nutrient availability induced by drought or intercropping.

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“…Plant research is important because we need to find ways to feed a growing population whilst limiting damage to the environment (1). Plant studies often involve the measurement of traits from images, which may be used in phenotyping for genome-wide association studies (2), comparing cultivars for traditional breeding (3) or testing a hypothesis related to plant physiology (4). Plant image analysis has been identified as a bottleneck in plant research (5).…”

Section: Introductionmentioning

confidence: 99%

RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation

Smith

Han

Petersen

et al. 2020

Preprint

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We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semiautomatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model. Deep Learning | GUI | Segmentation | Phenotyping | Biopore | Rhizotron | Root nodule | Interactive segmentation Correspondence: ags@di.ku.dk Fig. 1. RootPainter corrective annotation concept. (a) Roots in soil. (b) AI root predictions. (c) Human corrections. (d) AI learns from corrections. Smith et al. | bioRχiv | April 16, 2020 | 1-16

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Uptake of subsoil water below 2 m fails to alleviate drought response in deep-rooted Chicory (Cichorium intybus L.)

Cited by 34 publications

References 63 publications

Segmentation of roots in soil with U-Net

Segmentation of roots in soil with U-Net

The effect of drought and intercropping on chicory nutrient uptake from below 2 m studied in a multiple tracer setup

RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation

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