Plant Methods

Journal: Plant Methods

Abbreviation

Plant methods

Publisher

BioMed Central

ISSN

1746-4811

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Publications 1 - 10 of 31

Comparison of visible imaging, thermography and spectrometry methods to evaluate the effect of Heterodera schachtii inoculation on sugar beets
Joalland, Samuel; Screpanti, Claudio; Liebisch, Frank; et al. (2017)
Plant Methods
Background Phenotyping technologies are expected to provide predictive power for a range of applications in plant and crop sciences. Here, we use the disease pressure of Beet Cyst Nematodes (BCN) on sugar beet as an illustrative example to test the specific capabilities of different methods. Strong links between the above and belowground parts of sugar beet plants have made BCN suitable targets for use of non-destructive phenotyping methods. We compared the ability of visible light imaging, thermography and spectrometry to evaluate the effect of BCN on the growth of sugar beet plants. Results Two microplot experiments were sown with the nematode susceptible cultivar Aimanta and the nematode tolerant cultivar BlueFox under semi-field conditions. Visible imaging, thermal imaging and spectrometry were carried out on BCN infested and non-infested plants at different times during the plant development. Effects of a chemical nematicide were also evaluated using the three phenotyping methods. Leaf and beet biomass were measured at harvest. For both susceptible and tolerant cultivar, canopy area extracted from visible images was the earliest nematode stress indicator. Using such canopy area parameter, delay in leaf growth as well as benefit from a chemical nematicide could be detected already 15 days after sowing. Spectrometry was suitable to identify the stress even when the canopy reached full coverage. Thermography could only detect stress on the susceptible cultivar. Spectral Vegetation Indices related to canopy cover (NDVI and MCARI2) and chlorophyll content (CHLG) were correlated with the final yield (R = 0.69 on average for the susceptible cultivar) and the final nematode population in the soil (R = 0.78 on average for the susceptible cultivar). Conclusion In this paper we compare the use of visible imaging, thermography and spectrometry over two cultivars and 2 years under outdoor conditions. The three different techniques have their specific strengths in identifying BCN symptoms according to the type of cultivars and the growth stages of the sugar beet plants. Early detection of nematicide benefit and high yield predictability using visible imaging and spectrometry suggests promising applications for agricultural research and precision agriculture.
PhenoFly Planning Tool: Flight planning for high-resolution optical remote sensing with unmanned areal systems
Roth, Lukas; Hund, Andreas; Aasen, Helge (2018)
Plant Methods
Background Driven by a huge improvement in automation, unmanned areal systems (UAS) are increasingly used for field observations and high-throughput phenotyping. Today, the bottleneck does not lie in the ability to fly a drone anymore, but rather in the appropriate flight planning to capture images with sufficient quality. Proper flight preparation for photography with digital frame cameras should include relevant concepts such as view, sharpness and exposure calculations. Additionally, if mapping areas with UASs, one has to consider concepts related to ground control points (GCPs), viewing geometry and way-point flights. Unfortunately, non of the available flight planning tools covers all these aspects. Results We give an overview of concepts related to flight preparation, present the newly developed open source software PhenoFly Planning Tool, and evaluate other recent flight planning tools. We find that current flight planning and mapping tools strongly focus on vendor-specific solutions and mostly ignore basic photographic properties—our comparison shows, for example, that only two out of thirteen evaluated tools consider motion blur restrictions, and none of them depth of field limits. In contrast, PhenoFly Planning Tool enhances recent sophisticated UAS and autopilot systems with an optical remote sensing workflow that respects photographic concepts. The tool can assist in selecting the right equipment for your needs, experimenting with different flight settings to test the performance of the resulting imagery, preparing the field and GCP setup, and generating a flight path that can be exported as waypoints to be uploaded to an UAS. Conclusion By considering the introduced concepts, uncertainty in UAS-based remote sensing and high-throughput phenotyping may be considerably reduced. The presented software PhenoFly Planning Tool (https://shiny.usys.ethz.ch/PhenoFlyPlanningTool) helps users to comprehend and apply these concepts.
Multivariate unmixing approaches on Raman images of plant cell walls: New insights or overinterpretation of results?
Prats-Mateu, Batirtze; Felhofer, Martin; de Juan, Anna; et al. (2018)
Plant Methods
Background: Plant cell walls are nanocomposites based on cellulose microfibrils embedded in a matrix of polysaccharides and aromatic polymers. They are optimized for different functions (e.g. mechanical stability) by changing cell form, cell wall thickness and composition. To reveal the composition of plant tissues in a non-destructive way on the microscale, Raman imaging has become an important tool. Thousands of Raman spectra are acquired, each one being a spatially resolved molecular fingerprint of the plant cell wall. Nevertheless, due to the multicomponent nature of plant cell walls, many bands are overlapping and classical band integration approaches often not suitable for imaging. Multivariate data analysing approaches have a high potential as the whole wavenumber region of all thousands of spectra is analysed at once. Results: Three multivariate unmixing algorithms, vertex component analysis, non-negative matrix factorization and multivariate curve resolution–alternating least squares were applied to find the purest components within datasets acquired from micro-sections of spruce wood and Arabidopsis. With all three approaches different cell wall layers (including tiny S1 and S3 with 0.09–0.14 µm thickness) and cell contents were distinguished and endmember spectra with a good signal to noise ratio extracted. Baseline correction influences the results obtained in all methods as well as the way in which algorithm extracts components, i.e. prioritizing the extraction of positive endmembers by sequential orthogonal projections in VCA or performing a simultaneous extraction of non-negative components aiming at explaining the maximum variance in NMF and MCR-ALS. Other constraints applied (e.g. closure in VCA) or a previous principal component analysis filtering step in MCR-ALS also contribute to the differences obtained. Conclusions: VCA is recommended as a good preliminary approach, since it is fast, does not require setting many input parameters and the endmember spectra result in good approximations of the raw data. Yet the endmember spectra are more correlated and mixed than those retrieved by NMF and MCR-ALS methods. The latter two give the best model statistics (with lower lack of fit in the models), but care has to be taken about overestimating the rank as it can lead to artificial shapes due to peak splitting or inverted bands.
PlantDB - a versatile database for managing plant research
Exner, Vivien; Hirsch-Hoffmann, Matthias; Gruissem, Wilhelm; et al. (2008)
Plant Methods
Background Research in plant science laboratories often involves usage of many different species, cultivars, ecotypes, mutants, alleles or transgenic lines. This creates a great challenge to keep track of the identity of experimental plants and stored samples or seeds. Results Here, we describe PlantDB – a Microsoft® Office Access database – with a user-friendly front-end for managing information relevant for experimental plants. PlantDB can hold information about plants of different species, cultivars or genetic composition. Introduction of a concise identifier system allows easy generation of pedigree trees. In addition, all information about any experimental plant – from growth conditions and dates over extracted samples such as RNA to files containing images of the plants – can be linked unequivocally. Conclusion We have been using PlantDB for several years in our laboratory and found that it greatly facilitates access to relevant information.
SYMPATHIQUE: image-based tracking of symptoms and monitoring of pathogenesis to decompose quantitative disease resistance in the field
Anderegg, Jonas; Zenkl, Radek; Kirchgessner, Norbert; et al. (2024)
Plant Methods
Background Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.
A method for rapid and homogenous initiation of post-harvest physiological deterioration in cassava storage roots identifies Indonesian cultivars with improved shelf-life performance
Zainuddin, Ima M.; Lecart, Brieuc; Sudarmonowati, Enny; et al. (2023)
Plant Methods
Cassava is the most cultivated and consumed root crop in the world. One of the major constraints to the cassava value chain is the short shelf life of cassava storage roots which is primarily due to the so-called post-harvest physiological deterioration (PPD). The identification of natural sources of PPD tolerance represents a key approach to mitigating PPD losses by generating farmer- and industry-preferred cassava cultivars with prolonged shelf life. In the present study, a PPD assessment method was developed to screen for PPD tolerance in the cassava germplasm. The proposed PPD assessment method displayed a reduced rate of microbial infection and allowed a rapid and homogenous development of typical PPD symptoms in the cassava storage roots. We successfully used the PPD assessment method in combination with an image-based PPD scoring method to identify and characterize PPD tolerance in 28 cassava cultivars from the Indonesian cassava germplasm. Our analysis showed a significant and positive correlation between PPD score and dry matter content (r = 0.589–0.664, p-value < 0.001). Analysis of additional root parameters showed a significant and positive correlation between PPD scores at 2 days post-harvest (dph) and root length (r = 0.388, p-value < 0.05). Our analysis identified at least 4 cultivars displaying a significantly delayed onset of PPD symptoms as compared to the other selected cultivars. The availability of cassava cultivars contrasting for tolerance to PPD will be particularly instrumental to understanding the molecular mechanisms associated with delayed PPD in cassava roots.
Controlled vocabularies for plant anatomical parts optimized for use in data analysis tools and for cross-species studies
Meskauskiene, Rasa; Laule, Oliver; Ivanov, Nikolai V.; et al. (2013)
Plant Methods
Background It is generally accepted that controlled vocabularies are necessary to systematically integrate data from various sources. During the last decade, several plant ontologies have been developed, some of which are community specific or were developed for a particular purpose. In most cases, the practical application of these ontologies has been limited to systematically storing experimental data. Due to technical constraints, complex data structures and term redundancies, it has been difficult to apply them directly into analysis tools. Results Here, we describe a simplified and cross-species compatible set of controlled vocabularies for plant anatomy, focussing mainly on monocotypledonous and dicotyledonous crop and model plants. Their content was designed primarily for their direct use in graphical visualization tools. Specifically, we created annotation vocabularies that can be understood by non-specialists, are minimally redundant, simply structured, have low tree depth, and we tested them practically in the frame of Genevestigator. Conclusions The application of the proposed ontologies enabled the aggregation of data from hundreds of experiments to visualize gene expression across tissue types. It also facilitated the comparison of expression across species. The described controlled vocabularies are maintained by a dedicated curation team and are available upon request.
FruitPhenoBox – a device for rapid and automated fruit phenotyping of small sample sizes
Kirchgessner, Norbert; Hodel, Marius; Studer, Bruno; et al. (2024)
Plant Methods
Background: Fruit appearance of apple (Malus domestica Borkh.) is accession-specific and one of the main criteria for consumer choice. Consequently, fruit appearance is an important selection criterion in the breeding of new cultivars. It is also used for the description of older varieties or landraces. In commercial apple production, sorting devices are used to classify large numbers of fruit from a few cultivars. In contrast, the description of fruit from germplasm collections or breeding programs is based on only a few fruit from many accessions and is mostly performed visually by pomology experts. Such visual ratings are laborious, often difficult to compare and remain subjective. Results: Here we report on a morphometric device, the FruitPhenoBox, for automated fruit weighing and appearance description using computer-based analysis of five images per fruit. Recording of approximately 100 fruit from each of 15 apple cultivars using the FruitPhenoBox was rapid, with an average handling and recording time of less than eleven seconds per fruit. Comparison of fruit images from the 15 apple cultivars identified significant differences in shape index, fruit width, height and weight. Fruit shape was characteristic for each cultivar, while fruit color showed larger variation within sample sets. Assessing a subset of 20 randomly selected fruit per cultivar, fruit height, width and weight were described with a relative margin of error of 2.6%, 2.2%, and 6.2%, respectively, calculated from the mean value of all available fruit. Conclusions: The FruitPhenoBox allows for the rapid and consistent description of fruit appearance from individual apple accessions. By relating the relative margin of error for fruit width, height and weight description with different sample sizes, it was possible to determine an appropriate fruit sample size to efficiently and accurately describe the recorded traits. Therefore, the FruitPhenoBox is a useful tool for breeding and the description of apple germplasm collections.
A zoom into the nanoscale texture of secondary cell walls
Keplinger, Tobias; Konnerth, Johannes; Aguié-Béghin, Véronique; et al. (2014)
Plant Methods
Background Besides classical utilization of wood and paper, lignocellulosic biomass has become increasingly important with regard to biorefinery, biofuel production and novel biomaterials. For these new applications the macromolecular assembly of cell walls is of utmost importance and therefore further insights into the arrangement of the molecules on the nanolevel have to be gained. Cell wall recalcitrance against enzymatic degradation is one of the key issues, since an efficient degradation of lignocellulosic plant material is probably the most crucial step in plant conversion to energy. A limiting factor for in-depth analysis is that high resolution characterization techniques provide structural but hardly chemical information (e.g. Transmission Electron Microscopy (TEM), Atomic Force Microscopy (AFM)), while chemical characterization leads to a disassembly of the cell wall components or does not reach the required nanoscale resolution (Fourier Tranform Infrared Spectroscopy (FT-IR), Raman Spectroscopy). Results Here we use for the first time Scanning Near-Field Optical Microscopy (SNOM in reflection mode) on secondary plant cell walls and reveal a segmented circumferential nanostructure. This pattern in the 100 nm range was found in the secondary cell walls of a softwood (spruce), a hardwood (beech) and a grass (bamboo) and is thus concluded to be consistent among various plant species. As the nanostructural pattern is not visible in classical AFM height and phase images it is proven that the contrast is not due to changes in surfaces topography, but due to differences in the molecular structure. Conclusions Comparative analysis of model substances of casted cellulose nanocrystals and spin coated lignin indicate, that the SNOM signal is clearly influenced by changes in lignin distribution or composition. Therefore and based on the known interaction of lignin and visible light (e.g. fluorescence and resonance effects), we assume the elucidated nanoscale structure to reflect variations in lignification within the secondary cell wall.
Plant phenotyping: from bean weighing to image analysis
Walter, Achim; Liebisch, Frank; Hund, Andreas (2015)
Plant Methods
Plant phenotyping refers to a quantitative description of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Today, rapid developments are taking place in the field of non-destructive, image-analysis -based phenotyping that allow for a characterization of plant traits in high-throughput. During the last decade, ‘the field of image-based phenotyping has broadened its focus from the initial characterization of single-plant traits in controlled conditions towards ‘real-life’ applications of robust field techniques in plant plots and canopies. An important component of successful phenotyping approaches is the holistic characterization of plant performance that can be achieved with several methodologies, ranging from multispectral image analyses via thermographical analyses to growth measurements, also taking root phenotypes into account.

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Journal: Plant Methods

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