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Three-dimensional nuclear magnetic resonance image recorded from the growing plant in soil (A) and RGB reference image after washing off the soil (B) of a healthy sugar beet root system 56 d after inoculation. Field of view, 76.8×25.6×25.6 mm; resolution, 200×200×400 μm. TE=4 ms, TR=300 ms, measured at 4.7 T.
Source publication
Belowground symptoms of sugar beet caused by the beet cyst nematode (BCN) Heterodera schachtii include the development of compensatory secondary roots and beet deformity, which, thus far, could only be assessed by destructively
removing the entire root systems from the soil. Similarly, the symptoms of Rhizoctonia crown and root rot (RCRR) caused by...
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Citations
... "Plant illnesses and products can be detected using nuclear magnetic resonance (NMR) and Xray imaging techniques" [19]. "Internal bruising and Spraing disease signs in potato tubers were investigated using NMR imaging, as well as the difference between belowground damage caused by Heterodera schachtii and Rhizoctonia solani in sugar beets" [20,21]. "An additional underlying layer of melon seeds infected with Cucumber green mottle mosaic virus was discovered using optical coherence tomography" [22]. ...
Pests and pathogens inflict enormous financial harm on the global farming industry. Monitoring plant health and early pathogen detection is essential for facilitating successful management strategies and preventing the spread of disease. Various traditional methods and serological techniques have been found to be time-consuming and require handling skill. Also, the reliability of the result is uncertain, and it is hard to diagnose the pathogen during asymptomatic stages. Hence, the innovative sensors based on host reactions assessment, phage display-based biosensors, and bio-photonics in combination with other systems, remote sensing techniques integrated with spectroscopy-based approaches allow for high spatialization of data; these techniques could mainly be of immediate benefit for initial identification of infection and early control with limiting the use of Systemic Fungicides and developing a sustainable environment with high yield.
... C.Hillnhütter et al. used MRI to non-invasively detect subsurface symptoms of sugar beet crown and root rot caused by sugar beet cyst nematodes and rhizobia. Lateral root development and sugar beet deformation were evident on MRI images of beet cyst nematode-infected plants 28 days after inoculation compared to uninfected plants (Hillnhuetter et al., 2012). Nowadays, some scholars have used deep learning and transfer learning to segment the plant root system and detect plant disease based on leaf image data. ...
Verticillium wilt (VW) is often referred to as the cancer of cotton and it has a detrimental effect on cotton yield and quality. Since the root system is the first to be infested, it is feasible to detect VW by root analysis in the early stages of the disease. In recent years, with the update of computing equipment and the emergence of large-scale high-quality data sets, deep learning has achieved remarkable results in computer vision tasks. However, in some specific areas, such as cotton root MRI image task processing, it will bring some challenges. For example, the data imbalance problem (there is a serious imbalance between the cotton root and the background in the segmentation task) makes it difficult for existing algorithms to segment the target. In this paper, we proposed two new methods to solve these problems. The effectiveness of the algorithms was verified by experimental results. The results showed that the new segmentation model improved the Dice and mIoU by 46% and 44% compared with the original model. And this model could segment MRI images of rapeseed root cross-sections well with good robustness and scalability. The new classification model improved the accuracy by 34.9% over the original model. The recall score and F1 score increased by 59% and 42%, respectively. The results of this paper indicate that MRI and deep learning have the potential for non-destructive early detection of VW diseases in cotton.
... However, magnetic material found in some soils tends to create significant distortions in the images when operating at high field, especially in soil with more than 10% clay content (Pflugfelder et al., 2017), or more than 4% paramagnetic material by weight (Dusschoten et al., 2016). High-field MRI systems are capable of creating high-quality 3-D root system architecture images and generate root phenotyping data (Gruwel, 2014;Hillnhutter et al., 2012;Metzner et al., 2015). However, due to the aforementioned soil constraints, coupled with high power demands and MRI sensitivity to environmental radio frequency (RF) noise, these systems are often overlooked for root imaging. ...
Root phenotyping provides critical information to plant breeders for developing varieties with improved drought tolerance, greater root biomass, and greater nutrient use efficiency. Phenotyping roots in the natural environment is important for understanding the effect of the soil environment on root genotypic expressions. The goal of this work was to design and test a field‐scale mobile low‐field magnetic resonance imaging (LF‐MRI) Rhizotron that produces actionable root phenotyping data. We demonstrated this novel technology for root visualization and quantification using a LF‐MRI Rhizotron operating at 47 mT with two soil types. The LF‐MRI Rhizotron weights 453 kg, with a height of 90 cm, a diameter of 28 cm and an imaging field of view of 28 cm × 28 cm. The unit was operated in a Belk clay (Entic Hapluderts) and Weswood silt loam (Udifluventic Halustepts) generating 2‐D and 3‐D image data sets. The 2‐D image data had a collection time of 16.5 min per image at an image resolution of 2.2 mm per pixel. The 3‐D data had a collection time of 13 h per image with a 2.2 × 2.2 × 2.2 mm voxel resolution. Low‐field magnetic resonance imaging worked well for visualizing roots in moderate to high clay soils, demonstrating the potential for this technology; however, the broad application of this platform is hampered due to the prohibitively long scanning time to obtain 3‐D images. By increasing the field strength, and therefore the signal‐to‐noise ratio, faster scan times can enable a more useful system for root phenotyping.
... One application is root imaging as an additional value in plant phenotyping [18]. MRI has been used to monitor the development of three-dimensional (3D) root architecture [18], to identify active roots for water uptake [19], and to evaluate pathogen-induced root damage [20]. In addition, 3D MRI images have allowed comparison of the internal physiology of chilled and non-chilled tulip bulbs during storage and after planting [21,22]. ...
Background
Drought is a major consequence of global heating that has negative impacts on agriculture. Potato is a drought-sensitive crop; tuber growth and dry matter content may both be impacted. Moreover, water deficit can induce physiological disorders such as glassy tubers and internal rust spots. The response of potato plants to drought is complex and can be affected by cultivar type, climatic and soil conditions, and the point at which water stress occurs during growth. The characterization of adaptive responses in plants presents a major phenotyping challenge. There is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping.
Results
This project aimed to take advantage of innovative approaches in MRI, phenotyping and molecular biology to evaluate the effects of water stress on potato plants during growth. Plants were cultivated in pots under different water conditions. A control group of plants were cultivated under optimal water uptake conditions. Other groups were cultivated under mild and severe water deficiency conditions (40 and 20% of field capacity, respectively) applied at different tuber growth phases (initiation, filling). Water stress was evaluated by monitoring soil water potential. Two fully-equipped imaging cabinets were set up to characterize plant morphology using high definition color cameras (top and side views) and to measure plant stress using RGB cameras. The response of potato plants to water stress depended on the intensity and duration of the stress. Three-dimensional morphological images of the underground organs of potato plants in pots were recorded using a 1.5 T MRI scanner. A significant difference in growth kinetics was observed at the early growth stages between the control and stressed plants. Quantitative PCR analysis was carried out at molecular level on the expression patterns of selected drought-responsive genes. Variations in stress levels were seen to modulate ABA and drought-responsive ABA-dependent and ABA-independent genes.
Conclusions
This methodology, when applied to the phenotyping of potato under water deficit conditions, provides a quantitative analysis of leaves and tubers properties at microstructural and molecular levels. The approaches thus developed could therefore be effective in the multi-scale characterization of plant response to water stress, from organ development to gene expression.
... Lobet et al. established the Root System Markup Language (RSML) as a standard format to store any type of root architecture data, including root system topology, geometric pattern, local properties, and even gene expression information ( Figure 5E) (Rellán-Álvarez et al., 2015). Using 3D image datasets produced by magnetic resonance imaging (MRI), root-nematode interactions (Hillnhütter et al., 2012), root development (van Dusschoten et al., 2016), and root mobility (Rokitta et al., 1999) have been investigated at the macroscale. The effect of soil and substrate density on root architecture (Rogers et al., 2016;Burr-Hersey et al., 2017), root-root interactions (Mairhofer et al., 2015), and rootfungal interactions were examined using different types of computed tomography. ...
In multicellular and even single-celled organisms, individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation. Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes. Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project. In plant science, network analysis has similarly been applied to study the connectivity of plant components at the molecular, subcellular, cellular, organic, and organism levels. Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype. In this review, we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities. We next discussed the currently available analytic meth-odologies and multi-level imaging techniques used to map multiscale networks in plants. Finally, we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field. multiscale network, connectivity, cytoskeleton, membrane contact site, organelle interaction, multicellularity, con-nectome, cytoarchitecture, topological analysis, multi-level imaging techniques
... One application is root imaging as an additional value in plant phenotyping [19]. MRI has been used to monitor the development of 3D root architecture [19], to identify active roots for water uptake [20], and to evaluate pathogen-induced root damage [21]. In addition, 3D MRI images have allowed comparison of the internal physiology of chilled and non-chilled tulip bulbs during storage and after planting [22,23]. ...
Background: Drought is a major consequence of global heating that has negative impacts on agriculture. Potato is a drought-sensitive crop; tuber growth and dry matter content may both be impacted. Moreover, water deficit can induce physiological disorders such as glassy tubers and internal rust spots. The response of potato plants to drought is complex and can be affected by cultivar type, climatic and soil conditions, and the point at which water stress occurs during growth. The characterization of adaptive responses in plants presents a major phenotyping challenge. There is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping.
Results: This project aimed to take advantage of innovative approaches in MRI, phenotyping and molecular biology to evaluate the effects of water stress on potato plants during growth. Plants were cultivated in pots under different water conditions. A control group of plants were cultivated under optimal water uptake conditions. Other groups were cultivated under mild and severe water deficiency conditions (40 and 20% of field capacity, respectively) applied at different tuber growth phases (initiation, filling). Water stress was evaluated by monitoring soil water potential. Two fully-equipped imaging cabinets were set up to characterize plant morphology using high definition color cameras (top and side views) and to measure plant stress using RGB cameras. The response of potato plants to water stress depended on the intensity and duration of the stress. Three-dimensional morphological images of the underground organs of potato plants in pots were recorded using a 1.5 T MRI scanner. A significant difference in growth kinetics was observed at the early growth stages between the control and stressed plants. Quantitative PCR analysis was carried out at molecular level on the expression patterns of selected drought-responsive genes. Variations in stress levels were seen to modulate ABA and drought-responsive ABA-dependent and ABA-independent genes.
Conclusions: This methodology, when applied to the phenotyping of potato under water deficit conditions, provides a quantitative analysis of leaves and tubers properties at microstructural and molecular levels. The approaches thus developed could therefore be effective in the multi-scale characterization of plant response to water stress, from organ development to gene expression.
... Ultimately this information is processed into 3D image data sets. MRI has been used to examine root-nematode interactions (66), storage roots (101), and cereal root development (159), but it can also be used to view the distribution of root water uptake (149) and mobility (130,136,171). MRI is sensitive to the soil substrate, and distinguishing nuclear magnetic resonance signals originating from the water in soil versus the water in roots can be difficult. ...
The acquisition of quantitative information on plant development across a range of temporal and spatial scales is essential to understand the mechanisms of plant growth. Recent years have shown the emergence of imaging methodologies that enable the capture and analysis of plant growth, from the dynamics of molecules within cells to the measurement of morphometric and physiological traits in field-grown plants. In some instances, these imaging methods can be parallelized across multiple samples to increase throughput. When high throughput is combined with high temporal and spatial resolution, the resulting image-derived data sets could be combined with molecular large-scale data sets to enable unprecedented systems-level computational modeling. Such image-driven functional genomics studies may be expected to appear at an accelerating rate in the near future given the early success of the foundational efforts reviewed here. We present new imaging modalities and review how they have enabled a better understanding of plant growth from the microscopic to the macroscopic scale.
Expected final online publication date for the Annual Review of Plant Biology, Volume 71 is April 29, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... The cumbersome and destructive nature of this method made it difficult for researchers to properly record and analyze the growth of the roots. More recently, methods utilizing advanced imaging techniques such as magnetic resonance imaging (MRI) [19], X-ray computed tomography (CT) [20], positron emission tomography (PET) [21], and ultra-wideband imaging [9,22] have been in development for use in root phenotyping research. ...
... It only follows that MRI has found use in researching plant roots. It has been used to research the effect of pot size on root structure [23] and the effect of disease on sugar beet roots [19]. Unfortunately, MRI is not feasible for many places for use high throughput root phenotyping. ...
Understanding the root system architecture of plants as they develop is critical for increasing crop yields through plant phenotyping, and ultra-wideband imaging systems have shown potential as a portable, low-cost solution to non-destructive imaging root system architectures. This paper presents the design, implementation, and analysis of an ultra-wideband imaging system for use in imaging potted plant root system architectures. The proposed system is separated into three main subsystems: a Data Acquisition module, a Data Processing module, and an Image Processing and Analysis module. The Data Acquisition module consists of simulated and experimental implementations of a non-contact synthetic aperture radar system to measure ultra-wideband signal reflections from concealed scattering objects in a pot containing soil. The Data Processing module is responsible for interpreting the measured ultra-wideband signals and producing an image using a delay-and-sum beamforming algorithm. The Image Processing and Analysis module is responsible for improving image quality and measuring root depth and average root diameter in an unsupervised manner. The Image Processing and Analysis module uses a modified top-hat transformation alongside quantization methods based on energy distributions in the image to isolate the surface of the imaged root. Altogether, the proposed subsystems are capable of imaging and measuring concealed taproot system architectures with controlled soil conditions; however, the performance of the system is highly dependent on knowledge of the soil conditions. Smaller roots in difficult imaging conditions require future work into understanding and compensating for unwanted noise. Ultimately, this paper sought to provide insight into improving imaging quality of ultra-wideband (UWB) imaging systems for plant root imaging for other works to be followed.
... Below ground symptoms can traditionally be detected only by destructively removing the entire root system from the soil. Nuclear MRI was successfully applied to nondestructively identify symptoms caused by both RCRR (Fig. 7) and the beet cyst nematode Heterodera schachtii without removing plants from the growth media (Hillnhütter et al. 2012). Similarly, the effect of the Cercospora leaf spot disease on sugar beet taproot development was detected with MRI 14 days postinoculation, though differences in leaf size between plants with different levels of resistance were still small . ...
... The images were taken 28 days after inoculation. Reproduced with permission from Hillnhütter et al. (2012). ...
Plant phenomics approaches aim to measure traits such as growth, performance, and composition of plants using a suite of non-invasive technologies. The goal is to link phenotypic traits to the genetic information for particular genotypes, thus creating the bridge between the phenome and genome. Application of sensing technologies for detecting specific phenotypic reactions occurring during plant - pathogen interaction offers new opportunities for elucidating the physiological mechanisms that link pathogen infection and disease symptoms in the host, and also provides a faster approach in the selection of genetic material that is resistant to specific pathogens or strains. Appropriate phenomics methods and tools may also allow pre-symptomatic detection of disease-related changes in plants or to identify changes that are not visually apparent. This review focuses on the use of sensor-based phenomic tools in plant pathology such as those related to digital imaging, chlorophyll fluorescence imaging, spectral imaging, and thermal imaging. A brief introduction is provided for less often used approaches like magnetic resonance, soft x-ray imaging, ultrasound, and detection of volatile compounds. We hope that this concise review will stimulate further development and use of tools for automatic, non-destructive, and high-throughput phenotyping of plant-pathogen interaction.
... The basic principles of MRI are described in detail in several textbooks (Callaghan, 1993;Haacke et al., 1999) or review articles (Köckenberger et al., 2004;Blümler et al., 2009;van As et al., 2009;Borisjuk et al., 2012). Research applications to plant roots range from phytopathology (Hillnhütter et al., 2012), across storage root internal structures (Metzner et al., 2014) and water uptake modeling (Stingaciu et al., 2013), to coregistration with positron emission tomography for investigating structure-function relations . Water mobility in roots and soil has also been shown to be detectable with MRI (MacFall and Johnson, 2012;Gruwel, 2014). ...
Precise measurements of root system architecture traits are an important requirement for plant phenotyping. Most of the current methods for analyzing root growth either require artificial growing conditions (e.g. hydroponics), are severely restricted in the fraction of roots detectable (e.g. rhizotrons) or are destructive (e.g. soil coring). On the other hand, modalities such as magnetic resonance imaging (MRI) are noninvasive and allow high quality 3D imaging of roots in soil. Here, we present an automated plant root imaging and analysis pipeline using MRI together with an advanced image visualization and analysis software toolbox, named 'NMRooting'. Pots up to 117mm in diameter and 800mm in height can be measured with the 4.7T MRI instrument used here. For 1.5L pots (81mm diameter, 300mm high), a fully automated system was developed enabling measurement of up to 18 pots per day. The most important root traits which can be nondestructively monitored over time are root mass, length, diameter, tip number, growth angles (in 2D polar coordinates) and spatial distribution. Various validation measurements for these traits were performed showing that roots down to a diameter range between 200 and 300µm can be quantitatively measured. Root fresh weight correlates linearly with root mass determined by MRI. We demonstrate the capabilities of MRI and the dedicated imaging pipeline in experimental series performed on soil-grown maize (Zea mays) and barley (Hordeum vulgare) plants.
