Norbert Kirchgessner

Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach.

BackgroundField-based high throughput phenotyping is a bottleneck for crop breeding research. We present a novel method for repeated remote phenotyping of maize genotypes using the Zeppelin NT aircraft as an experimental sensor platform. The system has the advantage of a low altitude and cruising speed compared to many drones or airplanes, thus enhancing image resolution while reducing blurring effects. Additionally there was no restriction in sensor weight. Using the platform, red, green and blue colour space (RGB), normalized difference vegetation index (NDVI) and thermal images were acquired throughout the growing season and compared with traits measured on the ground. Ground control points were used to co-register the images and to overlay them with a plot map.ResultsNDVI images were better suited than RGB images to segment plants from soil background leading to two separate traits: the canopy cover (CC) and its NDVI value (NDVIPlant). Remotely sensed CC correlated well with plant density, early vigour, leaf size, and radiation interception. NDVIPlant was less well related to ground truth data. However, it related well to the vigour rating, leaf area index (LAI) and leaf biomass around flowering and to very late senescence rating. Unexpectedly, NDVIPlant correlated negatively with chlorophyll meter measurements. This could be explained, at least partially, by methodical differences between the used devices and effects imposed by the population structure. Thermal images revealed information about the combination of radiation interception, early vigour, biomass, plant height and LAI. Based on repeatability values, we consider two row plots as best choice to balance between precision and available field space. However, for thermography, more than two rows improve the precision.ConclusionsWe made important steps towards automated processing of remotely sensed data, and demonstrated the value of several procedural steps, facilitating the application in plant genetics and breeding. Important developments are: the ability to monitor throughout the season, robust image segmentation and the identification of individual plots in images from different sensor types at different dates. Remaining bottlenecks are: sufficient ground resolution, particularly for thermal imaging, as well as a deeper understanding of the relatedness of remotely sensed data and basic crop characteristics.

Large-Scale Proteomics of the Cassava Storage Root and Identification of a Target Gene to Reduce Postharvest Deterioration.

This work investigates the proteome changes during the onset of postharvest physiological deterioration (PPD) in cassava, an important root crop for food security in the tropics. The time-course proteomic study was instrumental in the identification of gene candidates to delay PPD in cassava roots. Cassava (Manihot esculenta) is the most important root crop in the tropics, but rapid postharvest physiological deterioration (PPD) of the root is a major constraint to commercial cassava production. We established a reliable method for image-based PPD symptom quantification and used label-free quantitative proteomics to generate an extensive cassava root and PPD proteome. Over 2600 unique proteins were identified in the cassava root, and nearly 300 proteins showed significant abundance regulation during PPD. We identified protein abundance modulation in pathways associated with oxidative stress, phenylpropanoid biosynthesis (including scopoletin), the glutathione cycle, fatty acid α-oxidation, folate transformation, and the sulfate reduction II pathway. Increasing protein abundances and enzymatic activities of glutathione-associated enzymes, including glutathione reductases, glutaredoxins, and glutathione S-transferases, indicated a key role for ascorbate/glutathione cycles. Based on combined proteomics data, enzymatic activities, and lipid peroxidation assays, we identified glutathione peroxidase as a candidate for reducing PPD. Transgenic cassava overexpressing a cytosolic glutathione peroxidase in storage roots showed delayed PPD and reduced lipid peroxidation as well as decreased H2O2 accumulation. Quantitative proteomics data from ethene and phenylpropanoid pathways indicate additional gene candidates to further delay PPD. Cassava root proteomics data are available at www.pep2pro.ethz.ch for easy access and comparison with other proteomics data.

Quantitative mapping of averaged focal adhesion dynamics in migrating cells by shape normalization.

The spatially ordered formation and disassembly of focal adhesions is a basic requirement for effective cell locomotion. Because focal adhesions couple the contractile actin–myosin network to the substrate, their distribution determines the pattern of traction forces propelling the cell in a certain direction. In the present study, we quantitatively analyzed the spatial patterning of cell–substrate adhesion in migrating cells by mapping averaged focal adhesion growth dynamics to a standardized cell coordinate system. These maps revealed distinct zones of focal adhesion assembly, disassembly and stability and were strongly interrelated with corresponding actin flow and traction force patterns. Moreover, the mapping technique enables precise detection of even minute responses of adhesion dynamics upon targeted signaling perturbations. For example, the partial inhibition of vinculin phosphorylation was followed by the reduced number of newly formed adhesions, whereas growth dynamics of existing adhesions remained unaffected.

The ETH field phenotyping platform FIP: A cable-suspended multi-sensor system

Crop phenotyping is a major bottleneck in current plant research. Field-based high-throughput phenotyping platforms are an important prerequisite to advance crop breeding. We developed a cable-suspended field phenotyping platform covering an area of ~1ha. The system operates from 2 to 5m above the canopy, enabling a high image resolution. It can carry payloads of up to 12kg and can be operated under adverse weather conditions. This ensures regular measurements throughout the growing period even during cold, windy and moist conditions. Multiple sensors capture the reflectance spectrum, temperature, height or architecture of the canopy. Monitoring from early development to maturity at high temporal resolution allows the determination of dynamic traits and their correlation to environmental conditions throughout the entire season. We demonstrate the capabilities of the system with respect to monitoring canopy cover, canopy height and traits related to thermal and multi-spectral imaging by selected examples from winter wheat, maize and soybean. The system is discussed in the context of other, recently established field phenotyping approaches; such as ground-operating or aerial vehicles, which impose traffic on the field or require a higher distance to the canopy.

Tube Width Fluctuations in F-Actin Solutions

We determine the statistics of the local tube width in F-actin solutions, beyond the usually reported mean value. Our experimental observations are explained by a segment fluid theory based on the binary collision approximation. In this systematic generalization of the standard mean-field approach, effective polymer segments interact via a potential representing the topological constraints. The analytically predicted universal tube width distribution with a stretched tail is in good agreement with the data.

Diel leaf growth of soybean: a novel method to analyze two-dimensional leaf expansion in high temporal resolution based on a marker tracking approach (Martrack Leaf)

BackgroundWe present a novel method for quantitative analysis of dicot leaf expansion at high temporal resolution. Image sequences of growing leaves were assessed using a marker tracking algorithm. An important feature of the method is the attachment of dark beads that serve as artificial landmarks to the leaf margin. The beads are mechanically constricted to the focal plane of a camera. Leaf expansion is approximated by the increase in area of the polygon defined by the centers of mass of the beads surrounding the leaf. Fluctuating illumination conditions often pose serious problems for tracking natural structures of a leaf; this problem is circumvented here by the use of the beads.ResultsThe new method has been used to assess leaf growth in environmental situations with different illumination conditions that are typical in agricultural and biological experiments: Constant illumination via fluorescent light tubes in a climate chamber, a mix of natural and artificial illumination in a greenhouse and natural illumination of the situation on typical summer days in the field. Typical features of diel (24h) soybean leaf growth patterns were revealed in all three conditions, thereby demonstrating the general applicability of the method. Algorithms are provided to the entire community interested in using such approaches.ConclusionsThe implementation Martrack Leaf presented here is a robust method to investigate diel leaf growth rhythms both under natural and artificial illumination conditions. It will be beneficial for the further elucidation of genotype x environment x management interactions affecting leaf growth processes.

Presenilin 1 Affects Focal Adhesion Site Formation and Cell Force Generation via c-Src Transcriptional and Posttranslational Regulation

Presenilin 1 and 2 (PS) are critical components of the γ-secretase complex that cleaves type I transmembrane proteins within their transmembrane domains. This process leads to release of proteolytically processed products from cellular membranes and plays an essential role in signal transduction or vital functions as cell adhesion. Here we studied the function of presenilins in cell-matrix interaction of wild-type and PS knock-out mouse embryonic fibroblasts. We found for PS1-/- cells an altered morphology with significantly reduced sizes of focal adhesion sites compared with wild type. Cell force analyses on micropatterned elastomer films revealed PS1-/- cell forces to be reduced by 50%. Pharmacological inhibition confirmed this function of γ-secretase in adhesion site and cell force formation. On the regulatory level, PS1 deficiency was associated with strongly decreased phosphotyrosine levels of focal adhesion site-specific proteins. The reduced tyrosine phosphorylation was caused by a down-regulation of c-Src kinase activity primarily at the level of c-Src transcription. The direct regulatory connection between PS1 and c-Src could be identified with ephrinB2 as PS1 target protein. Overexpression of ephrinB2 cytoplasmic domain resulted in its nuclear translocation with increased levels of c-Src and a full complementation of the PS1-/- adhesion and phosphorylation phenotype. Cleavage of full-length EB2 and subsequent intracellular domain translocation depended on PS1 as these processes were only found in WT cells. Therefore, we conclude that γ-secretase is vital for controlling cell adhesion and force formation by transcriptional regulation of c-Src via ephrinB2 cleavage.

High-resolution quantification of root dynamics in split-nutrient rhizoslides reveals rapid and strong proliferation of maize roots in response to local high nitrogen

Highlight Patches rich in nitrogen are rapidly colonized by selective root growth in maize, which was quantified at high time resolution with state-of-the-art non-invasive imaging techniques in a paper-based growth system.

Leaf Length Tracker: a novel approach to analyse leaf elongation close to the thermal limit of growth in the field

Highlight A novel weatherproof in situ method for high-precision leaf growth measurements in monocot plants was used to detect genotype-specific growth responses even at low temperature.

An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping

BackgroundRobust segmentation of canopy cover (CC) from large amounts of images taken under different illumination/light conditions in the field is essential for high throughput field phenotyping (HTFP). We attempted to address this challenge by evaluating different vegetation indices and segmentation methods for analyzing images taken at varying illuminations throughout the early growth phase of wheat in the field. 40,000 images taken on 350 wheat genotypes in two consecutive years were assessed for this purpose.ResultsWe proposed an image analysis pipeline that allowed for image segmentation using automated thresholding and machine learning based classification methods and for global quality control of the resulting CC time series. This pipeline enabled accurate classification of imaging light conditions into two illumination scenarios, i.e. high light-contrast (HLC) and low light-contrast (LLC), in a series of continuously collected images by employing a support vector machine (SVM) model. Accordingly, the scenario-specific pixel-based classification models employing decision tree and SVM algorithms were able to outperform the automated thresholding methods, as well as improved the segmentation accuracy compared to general models that did not discriminate illumination differences.ConclusionsThe three-band vegetation difference index (NDI3) was enhanced for segmentation by incorporating the HSV-V and the CIE Lab-a color components, i.e. the product images NDI3*V and NDI3*a. Field illumination scenarios can be successfully identified by the proposed image analysis pipeline, and the illumination-specific image segmentation can improve the quantification of CC development. The integrated image analysis pipeline proposed in this study provides great potential for automatically delivering robust data in HTFP.