Waterproofing in Arabidopsis: Following Phenolics and Lipids In situ by Confocal Raman Microscopy

Waterproofing of the aerial organs of plants imposed a big evolutionary step during the colonization of the terrestrial environment. The main plant polymers responsible of water repelling are lipids and lignin, which play also important roles in the protection against biotic/abiotic stresses, regulation of flux of gases and solutes and mechanical stability against negative pressure, among others. While the lipids, non-polymerized cuticular waxes together with the polymerized cutin, protect the outer surface, lignin is confined to the secondary cell wall within mechanical important tissues. In the present work a micro cross-section of the stem of Arabidopsis thaliana was used to track in situ the distribution of these non-carbohydrate polymers by Confocal Raman Microscopy. Raman hyperspectral imaging gives a molecular fingerprint of the native waterproofing tissues and cells with diffraction limited spatial resolution (~300 nm) at relatively high speed and without any tedious sample preparation. Lipids and lignified tissues as well as their effect on water content was directly visualized by integrating the 1299 cm-1, 1600 cm-1 and 3400 cm-1 band, respectively. For detailed insights into compositional changes of these polymers vertex component analysis was performed on selected sample positions. Changes have been elucidated in the composition of lignin within the lignified tissues and between interfascicular fibers and xylem vessels. Hydrophobising changes were revealed from the epidermal layer to the cuticle as well as a change in the aromatic composition within the cuticle of trichomes. To verify Raman signatures of different waterproofing polymers additionally Raman spectra of the cuticle and cutin monomer from tomato (Solanum lycopersicum) as well as aromatic model polymers (milled wood lignin and dehydrogenation polymer of coniferyl alcohol) and phenolic acids were acquired. Keywords: Arabidopsis thaliana, lignin, cutin, wax, Raman, cuticle, waterproofing, secondary cell wall, trichome

Multivariate unmixing approaches on Raman images of plant cell walls: new insights or overinterpretation of results?

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

Vibrational spectroscopy as a tool to understand plant silicification

Die Ablagerung von Siliziumdioxid ist ein verbreitetes Phänomen, das mit der Toleranz von Pflanzen gegenüber Belastungen korreliert. Die Pflanzen akkumulieren das amorphe Siliziumdioxid in mikroskopischen Partikeln, den Phytolithen, jedoch ist der exakte Mechanismus nicht vollständig aufgeklärt. Um ein besseres Verständnis über die Ablagerung von Siliziumdioxid zu erlangen, wurden verschiedene spektroskopische Techniken an Sorghumblättern und molekularen Modellen angewandt. Festkörper Kernspinresonanz und thermogravimetrische Analysen zeigen, dass die Siliziumdioxidstruktur von der Phytolithe-Extraktion abhängt. Basierend auf Raman- und IR-Daten einzelner Phytolithe lassen sich die Änderungen dieser Strukturen ermitteln. Das deutet auf unterschiedliche biologische Prozesse der Ablagerung des Siliciumdioxids hin. Die Pflanzengewebe in denen Siliciumdioxid abgelagert ist, wurden mit einem multimodalen Ansatz charakterisiert, welcher Fluoreszenz-, Hellfeld- und Rasterelektronenmikroskopie beinhaltet. Die chemische Zusammensetzung der Pflanzengewebe wurden mit Raman- und FTIR-Mikrospektroskopie kartiert. Ein neuartiger Ansatz zur Untersuchung von Pflanzengeweben wurde verwendet, basierend auf der optischen Nahfeldmikroskopie im mittleren IR-Bereich. Dieser ermöglicht eine kombinierte Analyse von mechanischen Materialeigenschaften sowie der chemischen Zusammensetzung und Struktur. Um die Rolle der organischen Matrix zu verstehen, wurden Modellverbindungen betrachtet, die die Ablagerung von Kieselsäure in den Pflanzen induzieren können. In-vitro-Reaktionen konnten eine gleichzeitige Präzipitation von Lignin und Siliciumdioxid sowie eine Polymerisation zusammen mit Peptiden simulieren. Die Ergebnisse lassen starke Wechselwirkungen zwischen diesen Verbindungen vermuten. Neben einem besseren Verständnis verschiedener Aspekte der Silifizierung von Pflanzen werden in dieser Arbeit neue Methoden zur Charakterisierung von Pflanzenproben vorgeschlagen.Silica deposition is a common phenomenon that correlates with plant tolerance to various stresses. Plants accumulate amorphous silica in microscopic particles termed phytoliths, through yet unclear mechanisms. With the aim to gain better understanding of the processes that govern silica deposition, different vibrational techniques were used on sorghum leaves and molecular models to obtain chemical and structural information addressing different length scales. Solid-state Nuclear Magnetic Resonance and thermogravimetric analysis showed that phytolith extraction methods affect silica structure. Nevertheless, Raman and IR analysis of individual phytoliths revealed differences in the structure and composition between phytolith types, suggesting the existence of different biological pathways for silica deposition. The environment of sorghum tissues where silica is deposited was assessed using a multimodal approach consisting of fluorescence, brightfield and scanning electron microscopies, while chemical composition was mapped using Raman and Fourier transformed Infrared microspectroscopy. Scattering-type near-field optical microscopy in the mid-infrared region was used to characterize the plant tissues, in both fixed and native plant samples. The nano-IR images and the mechanical phase image enabled a combined probing of mechanical material properties together with the chemical composition and structure of both the cell walls and the phytolith structures. In vitro reactions simulating lignin-silica co-precipitation and silica polymerization with peptides revealed strong interaction between these compounds and silica, and their possible involvement in silica deposition in the plant. This thesis provides a better understanding of the chemical process that control plant silicification, suggests new methodologies to characterize plant samples, and evaluates the current methods used in plant science

ADAPTIVE PROCESSING ARCHITECTURE OF MULTISENSOR SIGNALS FOR LOW-IMPACT TREATMENTS OF PLANT DISEASES.

Intelligent sensing for production of high-value crops Scientific and technical quality This thesis has been realized within the CROPS project. CROPS will develop scientific know-how for a highly configurable, modular and clever carrier platform that includes modular parallel manipulators and intelligent tools (sensors, algorithms, sprayers, grippers) that can be easily installed onto the carrier and are capable of adapting to new tasks and conditions. Several technological demonstrators will be developed for high value crops like greenhouse vegetables, fruits in orchards, and grapes for premium wines. The CROPS robotic platform will be capable of site-specific spraying (targets spray only towards foliage and selective targets) and selective harvesting of fruit (detects the fruit, determines its ripeness, moves towards the fruit, grasps it and softly detaches it). Another objective of CROPS is to develop techniques for reliable detection and classification of obstacles and other objects to enable successful autonomous navigation and operation in plantations and forests. The agricultural and forestry applications share many research areas, primarily regarding sensing and learning capabilities. The project started in October 2010 and will run for 48 month. The aim of this thesis is to lay the foundations, suggesting the guidelines, of one task addressed by the CROPS project, in particular, the aim of this work is to study the application of a VIS-NIR imaging approach (intelligent sensing), based on a relatively simple algorithm, to detect symptoms of powdery mildew and downy mildew disease at early stages of infection (sustainable production of high-value crops). Also a preliminary work for botrytis detection will be shown. Concept and objectives Many site-specific agricultural and forestry tasks, such as cultivating, transplanting, spraying, trimming, selective harvesting, and transportation, could be performed more efficiently if carried out by robotic systems. However, to date, agriculture and forestry robots are still not available, partly due to the complex, and often contradictory, demands for developing such systems. On the one hand, agro-forestry robots must be of reasonable cost, but on the other, they must be able to deal with complex, dynamic, and partly changing tasks. Addressing problems such as continuously changing conditions (e.g., rain and illumination), high variability in both the products (size, and shape) and the environment (location and soil properties), the delicate nature of the products, and hostile environmental conditions (e.g. dust, dirt, extreme temperature and humidity) requires advanced sensing, manipulation, and control. Since it is impossible to model a-priori all environments and task conditions, the robot must be able to learn new tasks and new working conditions. The solution to these demands lies in a modular and configurable design that will keep costs to a minimum by applying a basic configuration to a range of agricultural applications. At least a 95% yield rate is necessary for economical feasibility of an agro-forestry robotic system. Objectives An objective of CROPS project is to develop an \u201cintelligent tools\u201d (sensors, algorithms, sprayers) that can easily be installed onto a modular and clever carrier platform. The CROPS robotic platform will be capable of site-specific spraying (targeted spraying only on foliage and selected targets). Research efforts To achieve the novel systems described above, we will focus on intelligent sensing of disease detection on crop canopy (investigating different types and/or multiple sensors with decision making models). Technology evaluation Technology evaluation of the developed systems will include the performance evaluation of the different components (e.g., capacities, success rates/misses). Progress beyond the state-of-the-art Despite the extensive research conducted to date in applying robots to a variety of agriculture and forestry tasks (e.g., transplanting, spraying, trimming, selective harvesting), limited operating efficiencies (speeds, success rates) and lack of economic justification have severely limited commercialization. The few commercial autonomous agriculture and forestry robots that are available on the market include a cow milking robot, a robot for cutting roses (RomboMatic), and various remote-controlled forest harvesters. These robots either have a low level of autonomy or are able to perform only simple operations in structured and static environments (e.g. dairy farms and plant breeding facilities). Developing capabilities for robots operating in unstructured outdoor environments or dealing with the highly variable objects that exist in agriculture and forestry is still open-ended, and one of CROPS aims is to address this problem. Current state-of-the-art Field trials have routinely shown that most crop damage due to diseases and pests can be efficiently controlled when treatments are applied timely and accurately by hand to susceptible targets (i.e., by intelligent spraying). Site-specific spraying targeted solely to trees and/or to infected areas can reduce pesticide use by 20\u201340%. An issue of relevance to targeted agriculture is the detection of diseases in field crops. Since such events often have a visual manifestation, state-of-the-art methods for achieving this goal include fluorescence imaging or the analysis of spectral reflectance in carefully selected spectral bands. While reports of these methods used separately achieved performance at 75\u201390% accuracy, attempts to combine them have boosted disease discrimination accuracy to 95%. We must note here, however, that despite these promising results, very little research has been conducted on in-field disease detection. Expected progress The diseased detection approach for precision pesticide spraying will be developed investigating image processing techniques (after a laboratory spectral evaluation and greenhouse testing) for high-precision close-range targeted spraying to selectively and precisely apply chemicals solely to targets susceptible to specific diseases/pests, with a mean 90% success rate. Local changes in spectral reflection of parts of the canopy will be used as an indication of disease. \u201cSoft-sensor\u201d for detection of ripeness and diseases (noncontact rapid sensing system) will be developed by multispectral sensor (multispectral spectral camera). These \u201csoft sensor\u201d can be used as a decision model for targeted spraying

OCM 2021 - Optical Characterization of Materials

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

OCM 2021 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

Raman Mapping: Emerging Applications

Raman mapping is a noninvasive, label‐free technique with high chemical specificity and high potential to become a leading method in biological and biomedical applications. As opposed to Raman spectroscopy, which provides discrete chemical information at distinct positions within the sample, Raman mapping provides chemical information coupled with spatial information. The laser spot scans the investigated sample area with a preset step size and acquires Raman spectra pixel by pixel. The Raman spectra are then discriminated from each other by chemometric analysis, and the end result is a false color map, an image of the sample that contains highly precise structural and chemical information. Raman imaging has been successfully used for label‐free investigations at cellular and subcellular level. Cell compartments, cell responses to drugs and different stages of the cell cycle from the stem cell to the completely differentiated cell were successfully distinguished. This technique is also able to differentiate between healthy and cancer cells, indicating great potential for replacing conventional cancer detection tools with Raman detection in the future

Using UAV-Based Imagery to Determine Volume, Groundcover, and Growth Rate Characteristics of Lentil (Lens culinaris Medik.)

Plant growth rate is an essential phenotypic parameter for crop physiologists and plant breeders to understand in order to quantify potential crop productivity based on specific stages throughout the growing season. While plant growth rate information can be attained though manual collection of biomass, this procedure is rarely performed due to the prohibitively large effort and destruction of plant material that is required. Unmanned Aerial Vehicles (UAVs) offer great potential for rapid collection of imagery which can be utilized for quantification of plant growth rate. In this study, six diverse lines of lentil were grown in three replicates of microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery of each biomass collection time point was collected from a UAV and utilized to produce stitched two-dimensional orthomosaics and three-dimensional point clouds. Analysis of this imagery produced quantification of groundcover and vegetation volume on an individual plot basis. Comparison with manually-measured above-ground biomass suggests strong correlation, indicating great potential for UAVs to be utilized in plant breeding programs for evaluation of groundcover and vegetation volume. Nonlinear logistic models were fit to multiple data collection points throughout the growing season. The growth rate and G50, which is the number of growing degree days (GDD) required to accumulate 50 % of maximum growth, parameters of the model are capable of quantifying growth rate, and have potential utility in plant research and plant breeding programs. Predicted maximum volume was identified as a potential proxy for whole-plot biomass measurement. Six new phenotypes have been described that can be accurately and efficiently collected from field trials with the use of UAV’s or other overhead image-collection systems. These phenotypes are; Area Growth Rate, Area G50, Area Maximum Predicted Growth, Volume Growth Rate, Volume G50, and Volume Maximum Predicted Growth

Remote sensing of crop biophysical parameters for site-specific agriculture

xiv, 194 leaves : ill. (some col.) ; 29 cm.Support for sustainable agriculture by farmers and consumers is increasing as environmental and socio-economic issues rise due to more intensive farm practices. Site-specific crop management is an important component of sutainable agriculture, within which remote sensing can play an integral role. Field and image data were acquired over a farm in Saskatchewan as part of a national research project to demonstrate the advantages of site-specific agriculture for farmers. This research involved the estimation of crop biophysical parameters from airborne hyperspectral imagery using Spectral Mixture Analysis (SMA), a relatively new sub-pixel scale image processing method that derives the fraction of sunlit canopy, soil and shadow that is contributing to a pixel's relectance. SMA of three crop types (peas, wheat and canola) performed slightly better than conventional vegetation indices in predicting leaf area index (LAI) and biomass using Probe-1 imagery acquired early in the growing season. Other potential advantages for SMA were also indentified, and it was conclude that future research is warranted to assess the full potential of SMA in a multi-temporal sense throughout the growing season