Nejvíce citovaný článek - PubMed ID 24139902
Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant's physiological status. Therefore, using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments. We evaluated the use of 2 nondestructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350- to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic. Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view. The proximal canopy measurements were collected under natural solar light, using the same spectroradiometer with fiber optical cable to collect data on individual seedlings' hemispherical conical reflectance factor. The latter method was highly susceptible to changes in incoming radiation. Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range. Moreover, using random forest and support vector machine learning algorithms, the proximal data obtained from the top of the seedlings offered up to 83% accuracy in predicting 3 different Scots pine populations. We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.
- Publikační typ
- časopisecké články MeSH
The field of plant hormonomics focuses on the qualitative and quantitative analysis of the hormone complement in plant samples, akin to other omics sciences. Plant hormones, alongside primary and secondary metabolites, govern vital processes throughout a plant's lifecycle. While active hormones have received significant attention, studying all related compounds provides valuable insights into internal processes. Conventional single-class plant hormone analysis employs thorough sample purification, short analysis and triple quadrupole tandem mass spectrometry. Conversely, comprehensive hormonomics analysis necessitates minimal purification, robust and efficient separation and better-performing mass spectrometry instruments. This review summarizes the current status of plant hormone analysis methods, focusing on sample preparation, advances in chromatographic separation and mass spectrometric detection, including a discussion on internal standard selection and the potential of derivatization. Moreover, current approaches for assessing the spatiotemporal distribution are evaluated. The review touches on the legitimacy of the term plant hormonomics by exploring the current status of methods and outlining possible future trends.
- Klíčová slova
- Hormonomics, Internal standard, Liquid chromatography, Mass spectrometry, Matrix effect, Metabolomics, Omics, Plant hormone, Solid phase extraction,
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
BACKGROUND: Plants, like all living organisms, metamorphose their bodies during their lifetime. All the developmental and growth events in a plant's life are connected to specific points in time, be it seed germination, seedling emergence, the appearance of the first leaf, heading, flowering, fruit ripening, wilting, or death. The onset of automated phenotyping methods has brought an explosion of such time-to-event data. Unfortunately, it has not been matched by an explosion of adequate data analysis methods. RESULTS AND DISCUSSION: In this paper, we introduce the Bayesian approach towards time-to-event data in plant biology. As a model example, we use seedling emergence data of maize under control and stress conditions but the Bayesian approach is suitable for any time-to-event data (see the examples above). In the proposed framework, we are able to answer key questions regarding plant emergence such as these: (1) Do seedlings treated with compound A emerge earlier than the control seedlings? (2) What is the probability of compound A increasing seedling emergence by at least 5 percent? CONCLUSION: Proper data analysis is a fundamental task of general interest in life sciences. Here, we present a novel method for the analysis of time-to-event data which is applicable to many plant developmental parameters measured in field or in laboratory conditions. In contrast to recent and classical approaches, our Bayesian computational method properly handles uncertainty in time-to-event data and it is capable to reliably answer questions that are difficult to address by classical methods.
- Klíčová slova
- Bayesian inference, Data analysis, Plant development, Plant phenotyping, Statistics, Survival analysis, Time-to-event data, Uncertainty,
- Publikační typ
- časopisecké články MeSH
Plant phenotyping platforms offer automated, fast scoring of traits that simplify the selection of varieties that are more competitive under stress conditions. However, indoor phenotyping methods are frequently based on the analysis of plant growth in individual pots. We present a reproducible indoor phenotyping method for screening young barley populations under water stress conditions and after subsequent rewatering. The method is based on a simple read-out of data using RGB imaging, projected canopy height, as a useful feature for indirectly following the kinetics of growth and water loss in a population of barley. A total of 47 variables including 15 traits and 32 biochemical metabolites measured (morphometric parameters, chlorophyll fluorescence imaging, quantification of stress-related metabolites; amino acids and polyamines, and enzymatic activities) were used to validate the method. The study allowed the identification of metabolites related to water stress response and recovery. Specifically, we found that cadaverine (Cad), 1,3-aminopropane (DAP), tryptamine (Tryp), and tyramine (Tyra) were the major contributors to the water stress response, whereas Cad, DAP, and Tyra, but not Tryp, remained at higher levels in the stressed plants even after rewatering. In this work, we designed, optimized and validated a non-invasive image-based method for automated screening of potential water stress tolerance genotypes in barley populations. We demonstrated the applicability of the method using transgenic barley lines with different sensitivity to drought stress showing that combining canopy height and the metabolite profile we can discriminate tolerant from sensitive genotypes. We showed that the projected canopy height a sensitive trait that truly reflects other invasively studied morphological, physiological, and metabolic traits and that our presented methodological setup can be easily applicable for large-scale screenings in low-cost systems equipped with a simple RGB camera. We believe that our approach will contribute to accelerate the study and understanding of the plant water stress response and recovery capacity in crops, such as barley.
- Klíčová slova
- Hordeum vulgare, amino acids, antioxidative enzymes, blue (RGB) imaging, canopy height, fluorescence, green, indoor phenotyping, polyamines, red,
- Publikační typ
- časopisecké články MeSH
At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for "next generation phenomics" based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.
- Klíčová slova
- IPPN, Imaging, Metadata, Next generation phenomics, Plant phenotyping, Sensor development, Trait value,
- MeSH
- genomika metody MeSH
- šlechtění rostlin * MeSH
- zemědělské plodiny genetika MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Plant biostimulants which include bioactive substances (humic acids, protein hydrolysates and seaweed extracts) and microorganisms (mycorrhizal fungi and plant growth promoting rhizobacteria of strains belonging to the genera Azospirillum, Azotobacter, and Rhizobium spp.) are gaining prominence in agricultural systems because of their potential for improving nutrient use efficiency, tolerance to abiotic stressors, and crop quality. Highly accurate non-destructive phenotyping techniques have attracted the interest of scientists and the biostimulant industry as an efficient means for elucidating the mode of biostimulant activity. High-throughput phenotyping technologies successfully employed in plant breeding and precision agriculture, could prove extremely useful in unraveling biostimulant-mediated modulation of key quantitative traits and would also facilitate the screening process for development of effective biostimulant products in controlled environments and field conditions. This perspective article provides an innovative discussion on how small, medium, and large high-throughput phenotyping platforms can accelerate efforts for screening numerous biostimulants and understanding their mode of action thanks to pioneering sensor and image-based phenotyping techniques. Potentiality and constraints of small-, medium-, and large-scale screening platforms are also discussed. Finally, the perspective addresses two screening approaches, "lab to field" and "field to lab," used, respectively, by small/medium and large companies for developing novel and effective second generation biostimulant products.
Current methods of in-house plant phenotyping are providing a powerful new tool for plant biology studies. The self-constructed and commercial platforms established in the last few years, employ non-destructive methods and measurements on a large and high-throughput scale. The platforms offer to certain extent, automated measurements, using either simple single sensor analysis, or advanced integrative simultaneous analysis by multiple sensors. However, due to the complexity of the approaches used, it is not always clear what such forms of plant phenotyping can offer the potential end-user, i.e. plant biologist. This review focuses on imaging methods used in the phenotyping of plant shoots including a brief survey of the sensors used. To open up this topic to a broader audience, we provide here a simple introduction to the principles of automated non-destructive analysis, namely RGB, chlorophyll fluorescence, thermal and hyperspectral imaging. We further on present an overview on how and to which extent, the automated integrative in-house phenotyping platforms have been used recently to study the responses of plants to various changing environments.
- Klíčová slova
- Biomass production, Chlorophyll fluorescence imaging, Hyperspectral imaging, Plant phenotyping, RGB digital imaging, Shoot growth, Thermal imaging,
- Publikační typ
- časopisecké články MeSH
