Tracking Growth and Decay of Plant Roots in Minirhizotron Images

Gillert, Alexander; Peters, Bo; Lukas, Uwe Freiherr von; Kreyling, Jürgen; Blume‐Werry, Gesche

doi:10.1109/wacv56688.2023.00369

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…A couple studies have shown improved phenotyping prediction through added HS information [33, 34]. Researchers may also use the data to analyze root growth, architecture, and turnover of dense root systems [35, 36]. Some images contain other potential objects of interest such as fungus, mold, and algae which may be studied at their various timesteps to determine possible interactive dynamics between root and rhizosphere.…”

Section: Applicationsmentioning

confidence: 99%

“…We provide images for studying root traits in both peanut and sweet corn roots, and we apply a subset of peanut images to the task of semantic segmentation for both types of image data. Our dataset contains more in-depth evaluation of plant roots across a broad range of soil conditions, that can be applied to studies on phenotyping prediction [33, 34], dense root systems [35, 36], interactive dynamics between root and rhizosphere [37], and drought resiliency [5, 6]. The data can also be applied to the tasks of data reconstruction and semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Our dataset contains more in-depth evaluation of plant roots across a broad range of soil conditions that can be applied to studies on phenotyping prediction [33,34], dense root systems [35,36], interactive dynamics between root and rhizosphere [37], and drought resiliency [5,6]. Most recently, HyperPRI has been used for studying and comparing the crop plants' drought tolerance and recovery [38].…”

Section: Introductionmentioning

confidence: 99%

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HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

Chang,

Chowdhry,

Song

et al. 2023

Preprint

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Collecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants’ physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. In the field of machine learning (ML), there are currently no publicly available datasets containing the heavy correlation, highly textured background, and thin features characteristic of belowground root systems. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species – peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatial-spectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRI’s hyperspectral and spatial information improves semantic segmentation of target objects.

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Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

Chang,

Chowdhry,

Song

et al. 2023

Preprint

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“…In a recent collaboration within the DIG-IT! project, ecologists from the University of Greifswald and visual computing experts from the Fraunhofer Institute IGD in Rostock have explored the potential of automated image recognition for the study of wood anatomy, root growth, and bat monitoring (Gillert et al, 2023; Krivek et al, 2023; Peters et al, 2023; Resente et al, 2021). As a fourth focus, we investigated the automated recognition of subfossil pollen and spores in lake sediments.…”

Section: Introductionmentioning

confidence: 99%

A trainable object finder, selector and identifier for pollen, spores and other things: A step towards automated pollen recognition in lake sediments

Theuerkauf,

Siradze,

Gillert

2023

The Holocene

Self Cite

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Pollen records are the most important proxy for reconstructing past terrestrial vegetation. While new approaches for improved quantitative interpretation of pollen data have been developed over the last decades, the availability of pollen records remains mostly limited because pollen samples are still analysed manually, which is a time-consuming task and requires extensive training. Here, we present an approach for automated recognition of pollen and spores from lake sediments using deep convolutional neural networks and machine learning. The approach includes two stages. The detector first locates pollen and spores in the sample matrix, and the classifier then classifies these objects. We have trained the approach on two pollen datasets from two lakes in north-eastern Germany. So far, our approach is able to automatically recognise 10 pollen types and Lycopodium spores with high accuracy. As soon as more training data are available, more pollen and spore types can be added. The preparation of training data, the training of the neural networks and their application are accessible via a freely available, browser-based user interface called TOFSI.

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Toward improved image‐based root phenotyping: Handling temporal and cross‐site domain shifts in crop root segmentation models

Banet,

Smith,

McGrail

et al. 2024

The Plant Phenome Journal

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Crop root segmentation models developed through deep learning have increased the throughput of in situ crop phenotyping studies. However, models trained to identify roots in one image dataset may not accurately identify roots in another dataset, especially when the new dataset contains known differences, called domain shifts. The objective of this study was to quantify how model performance changes when models are used to segment image datasets that contain domain shifts and evaluate approaches to reduce error associated with domain shifts. We collected maize root images at two growth stages (V7 and R2) in a field experiment and manually segmented images to measure total root length (TRL). We developed five segmentation models and evaluated each model's ability to handle a temporal (growth‐stage) domain shift. For the V7 growth stage, a growth‐stage‐specific model trained only on images captured at the V7 growth stage was best suited for measuring TRL. At the R2 growth stage, combining images from both growth stages into a single dataset to train a model resulted in the most accurate TRL measurements. We applied two of the field models to images from a greenhouse experiment to evaluate how model performance changed when exposed to a cross‐site domain shift. Field models were less accurate than models trained only on the greenhouse images even when crop growth stage was identical. Although models may perform well for one experiment, model error increases when applied to images from different experiments even when crop species, growth stage, and soil type are similar.

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Tracking Growth and Decay of Plant Roots in Minirhizotron Images

Cited by 3 publications

References 22 publications

HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

A trainable object finder, selector and identifier for pollen, spores and other things: A step towards automated pollen recognition in lake sediments

Toward improved image‐based root phenotyping: Handling temporal and cross‐site domain shifts in crop root segmentation models

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