Hanno Scharr

Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants.

Stress caused by environmental factors evokes dynamic changes in plant phenotypes. In this study, we deciphered simultaneously the reaction of plant growth and chlorophyll fluorescence related parameters using a novel approach which combines existing imaging technologies (GROWSCREEN FLUORO). Three different abiotic stress situations were investigated demonstrating the benefit of this approach to distinguish between effects related to (1) growth, (2) chlorophyll-fluorescence, or (3) both of these aspects of the phenotype. In a drought stress experiment with more than 500 plants, poly(ADP-ribose) polymerase (PARP) deficient lines of Arabidopsis thaliana (L.) Heynh showed increased relative growth rates (RGR) compared with C24 wild-type plants. In chilling stress, growth of PARP and C24 lines decreased rapidly, followed by a decrease in Fv/Fm. Here, PARP-plants showed a more pronounced decrease of Fv/Fm than C24, which can be interpreted as a more efficient strategy for survival in mild chilling stress. Finally, the reaction of Nicotiana tabacum L. to altered spectral composition of the intercepted light was monitored as an example of a moderate stress situation that affects chlorophyll-fluorescence related, but not growth-related parameters. The examples investigated in this study show the capacity for improved plant phenotyping based on an automated and simultaneous evaluation of growth and photosynthesis at high throughput.

Temperature responses of roots: impact on growth, root system architecture and implications for phenotyping

Root phenotyping is a challenging task, mainly because of the hidden nature of this organ. Only recently, imaging technologies have become available that allow us to elucidate the dynamic establishment of root structure and function in the soil. In root tips, optical analysis of the relative elemental growth rates in root expansion zones of hydroponically-grown plants revealed that it is the maximum intensity of cellular growth processes rather than the length of the root growth zone that control the acclimation to dynamic changes in temperature. Acclimation of entire root systems was studied at high throughput in agar-filled Petri dishes. In the present study, optical analysis of root system architecture showed that low temperature induced smaller branching angles between primary and lateral roots, which caused a reduction in the volume that roots access at lower temperature. Simulation of temperature gradients similar to natural soil conditions led to differential responses in basal and apical parts of the root system, and significantly affected the entire root system. These results were supported by first data on the response of root structure and carbon transport to different root zone temperatures. These data were acquired by combined magnetic resonance imaging (MRI) and positron emission tomography (PET). They indicate acclimation of root structure and geometry to temperature and preferential accumulation of carbon near the root tip at low root zone temperatures. Overall, this study demonstrated the value of combining different phenotyping technologies that analyse processes at different spatial and temporal scales. Only such an integrated approach allows us to connect differences between genotypes obtained in artificial high throughput conditions with specific characteristics relevant for field performance. Thus, novel routes may be opened up for improved plant breeding as well as for mechanistic understanding of root structure and function.

QTL analysis of early stage heterosis for biomass in Arabidopsis

The main objective of this study was to identify genomic regions involved in biomass heterosis using QTL, generation means, and mode-of-inheritance classification analyses. In a modified North Carolina Design III we backcrossed 429 recombinant inbred line and 140 introgression line populations to the two parental accessions, C24 and Col-0, whose F1 hybrid exhibited 44% heterosis for biomass. Mid-parent heterosis in the RILs ranged from −31 to 99% for dry weight and from −58 to 143% for leaf area. We detected ten genomic positions involved in biomass heterosis at an early developmental stage, individually explaining between 2.4 and 15.7% of the phenotypic variation. While overdominant gene action was prevalent in heterotic QTL, our results suggest that a combination of dominance, overdominance and epistasis is involved in biomass heterosis in this Arabidopsis cross.

Optimal filters for extended optical flow

Estimation of optical flow and physically motivated brightness changes can be formulated as parameter estimation in linear models. Accuracy of this estimation heavily depends on the filter families used to implement the models. In this paper we focus on models whose terms are all data dependent and therefore are best estimated via total-least-squares (TLS) or similar estimators. Using three different linear models we derive model dependent optimality criteria based on transfer functions of filter families with given fixed size. Using a simple optimization procedure, we demonstrate typical properties of optimal filter sets for optical flow, simultaneous estimation of optical flow and diffusion, as well as optical flow and exponential decay. Exemplarily we show their performance and state some useful choices.

Study of Dynamical Processes with Tensor-Based Spatiotemporal Image Processing Techniques

Image sequence processing techniques are used to study exchange, growth, and transport processes and to tackle key questions in environmental physics and biology. These applications require high accuracy for the estimation of the motion field since the most interesting parameters of the dynamical processes studied are contained in first-order derivatives of the motion field or in dynamical changes of the moving objects. Therefore the performance and optimization of low-level motion estimators is discussed. A tensor method tuned with carefully optimized derivative filters yields reliable and dense displacement vector fields (DVF) with an accuracy of up to a few hundredth pixels/frame for real-world images. The accuracy of the tensor method is verified with computer-generated sequences and a calibrated image sequence. With the improvements in accuracy the motion estimation is now rather limited by imperfections in the CCD sensors, especially the spatial nonuniformity in the responsivity. With a simple two-point calibration, these effects can efficiently be suppressed. The application of the techniques to the analysis of plant growth, to ocean surface microturbulence in IR image sequences, and to sediment transport is demonstrated.

Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner]

Plant phenotyping is the identification of effects on the phenotype (i.e., the plant appearance and performance) as a result of genotype differences (i.e., differences in the genetic code) and the environmental conditions to which a plant has been exposed [1]?[3]. According to the Food and Agriculture Organization of the United Nations, large-scale experiments in plant phenotyping are a key factor in meeting the agricultural needs of the future to feed the world and provide biomass for energy, while using less water, land, and fertilizer under a constantly evolving environment due to climate change. Working on model plants (such as Arabidopsis), combined with remarkable advances in genotyping, has revolutionized our understanding of biology but has accelerated the need for precision and automation in phenotyping, favoring approaches that provide quantifiable phenotypic information that could be better used to link and find associations in the genotype [4]. While early on, the collection of phenotypes was manual, currently noninvasive, imaging-based methods are increasingly being utilized [5], [6]. However, the rate at which phenotypes are extracted in the field or in the lab is not matching the speed of genotyping and is creating a bottleneck [1].

Accurate optical flow in noisy image sequences

Optical flow estimation in noisy image sequences requires a special denoising strategy. Towards this end we introduce a new tensor-driven anisotropic diffusion scheme which is designed to enhance optical-flow-like spatio-temporal structures. This is achieved by selecting diffusivities in a special manner depending on the eigenvalues of the well known structure tensor. We illustrate how the proposed choice differs from edge- and coherence-enhancing anisotropic diffusion. Furthermore we extend a recently discovered discretization scheme for anisotropic diffusion to 3D data. An automatic stop criterion to terminate the diffusion after a suitable time is given. The performance of the introduced method is examined quantitatively using image sequences with a substantial amount of noise added.

Finely-grained annotated datasets for image-based plant phenotyping

First comprehensive annotated datasets for computer vision tasks in plant phenotyping.Publicly available data and evaluation criteria for eight challenging tasks.Tasks include fine-grained categorization of age, developmental stage, and cultivars.Example test cases and results on plant and leaf-wise segmentation and leaf counting. In this paper we present a collection of benchmark datasets for the development and evaluation of computer vision and machine learning algorithms in the context of plant phenotyping. We provide annotated imaging data and suggest suitable evaluation criteria for plant/leaf segmentation, detection, tracking as well as classification and regression problems. The Figure symbolically depicts the data available together with ground truth segmentations and further annotations and metadata.Display Omitted Image-based approaches to plant phenotyping are gaining momentum providing fertile ground for several interesting vision tasks where fine-grained categorization is necessary, such as leaf segmentation among a variety of cultivars, and cultivar (or mutant) identification. However, benchmark data focusing on typical imaging situations and vision tasks are still lacking, making it difficult to compare existing methodologies. This paper describes a collection of benchmark datasets of raw and annotated top-view color images of rosette plants. We briefly describe plant material, imaging setup and procedures for different experiments: one with various cultivars of Arabidopsis and one with tobacco undergoing different treatments. We proceed to define a set of computer vision and classification tasks and provide accompanying datasets and annotations based on our raw data. We describe the annotation process performed by experts and discuss appropriate evaluation criteria. We also offer exemplary use cases and results on some tasks obtained with parts of these data. We hope with the release of this rigorous dataset collection to invigorate the development of algorithms in the context of plant phenotyping but also provide new interesting datasets for the general computer vision community to experiment on. Data are publicly available at http://www.plant-phenotyping.org/datasets.

Numerische Isotropieoptimierung von FIR-Filtern mittels Querglättung

Richtungsunabhangige Filter fur Bilder mit mehreren Dimensionen werden in der digitalen Bildverarbeitung, z.B. in Textur- oder Bewegungsanalyse, benotigt. In diesem Paper werden Finite-Impulse-Response-Filter fur die erste und zweite Ableitung in zwei und drei Dimensionen optimiert. Dafur wird eine Norm einer Winkelfehlerfunktion, die den ganzen Ortsfrequenzraum einnimmt, mit numerischen Optimierungsverfahren minimiert. Durch eine Glattung der eindimensionalen Filter in allen Querrichtungen werden optimierbare Freiheitsgrade eingefuhrt. Dieser Ansatz fuhrt auf effiziente, d.h. kompakte und separable Filter. Da die Filter vom verwendeten Bildmaterial weitgehend unabhangig sind, konnen sie universell eingesetzt werden.

Diel Growth Cycle of Isolated Leaf Discs Analyzed with a Novel, High-Throughput Three-Dimensional Imaging Method Is Identical to That of Intact Leaves

Dicot leaves grow with pronounced diel (24-h) cycles that are controlled by a complex network of factors. It is an open question to what extent leaf growth dynamics are controlled by long-range or by local signals. To address this question, we established a stereoscopic imaging system, GROWSCREEN 3D, which quantifies surface growth of isolated leaf discs floating on nutrient solution in wells of microtiter plates. A total of 458 leaf discs of tobacco (Nicotiana tabacum) were cut at different developmental stages, incubated, and analyzed for their relative growth rates. The camera system was automatically displaced across the array of leaf discs; visualization and camera displacement took about 12 s for each leaf disc, resulting in a time interval of 1.5 h for consecutive size analyses. Leaf discs showed a comparable diel leaf growth cycle as intact leaves but weaker peak growth activity. Hence, it can be concluded that the timing of leaf growth is regulated by local rather than by systemic control processes. This conclusion was supported by results from leaf discs of Arabidopsis (Arabidopsis thaliana) Landsberg erecta wild-type plants and starch-free1 mutants. At night, utilization of transitory starch leads to increased growth of Landsberg erecta wild-type discs compared with starch-free1 discs. Moreover, the decrease of leaf disc growth when exposed to different concentrations of glyphosate showed an immediate dose-dependent response. Our results demonstrate that a dynamic leaf disc growth analysis as we present it here is a promising approach to uncover the effects of internal and external cues on dicot leaf development.