Towards remote sensing of vegetation processes

The latest advances in imaging spectroscopy of vegetation enabled remote sensing (RS) of plant reflected or emitted signals associated with photosynthetic processes as the photoprotective transformation of xanthophyll pigments or the chlorophyll fluorescence (Chl-F). A potential future European Space Agency (ESA) satellite mission FLEX is expected to sense, apart from other parameters, so-called steady-state chlorophyll fluorescence (Chl-FS) signal, which may be potentially used for monitoring of photosynthesis (vegetation canopy carbon assimilation rate). Nevertheless, geometric complexity of plant canopies and signal disturbing atmospheric factors require a proper approach for scaling the information of a single leaf optical properties up to the RS image data of anisotropic vegetation canopies. Such up-scaling approach can be established only via synergic measurements of ground based and air-/space-borne optical sensors. Our initial experiment revealed that Chl-FS, being strongly driven by the air temperature, is able to accurately indicate onset and off-set of the photosynthetically active period for the evergreen plants. Next field experiment, carried out with the VNIR imaging spectroradiometer AISA Eagle (SPECIM Ltd., Finland) mounted above the montane grassland and Norway spruce (Picea abies /L./ Karst.) canopies, showed that the fluorescence signal is retrievable from passive optical imaging spectroscopy data. Further analyses revealed that some of the vegetation \u27process-related\u27 optical indices (e.g., photochemical reflectance index - PRI) are closely correlated to the parameters measured over the experimental canopies by eddy-covariance flux systems. The future objective is to continue in development the leaf-canopy Chl-F up-scaling approach by setting up local scale experiments employing the field pocket-size cost effective instruments measuring the leaf optical indices and Chl-F parameters simultaneously with canopy reflectance acquired by RS sensors from tower and aircraft platforms

Dissecting the interaction of photosynthetic electron transfer with mitochondrial signalling and hypoxic response in the Arabidopsis rcd1 mutant

The Arabidopsis mutant rcd1 is tolerant to methyl viologen (MV). MV enhances the Mehler reaction, i.e. electron transfer from Photosystem I (PSI) to O-2, generating reactive oxygen species (ROS) in the chloroplast. To study the MV tolerance of rcd1, we first addressed chloroplast thiol redox enzymes potentially implicated in ROS scavenging. NADPH-thioredoxin oxidoreductase type C (NTRC) was more reduced in rcd1. NTRC contributed to the photosynthetic and metabolic phenotypes of rcd1, but did not determine its MV tolerance. We next tested rcd1 for alterations in the Mehler reaction. In rcd1, but not in the wild type, the PSI-to-MV electron transfer was abolished by hypoxic atmosphere. A characteristic feature of rcd1 is constitutive expression of mitochondrial dysfunction stimulon (MDS) genes that affect mitochondrial respiration. Similarly to rcd1, in other MDS-overexpressing plants hypoxia also inhibited the PSI-to-MV electron transfer. One possible explanation is that the MDS gene products may affect the Mehler reaction by altering the availability of O-2. In green tissues, this putative effect is masked by photosynthetic O-2 evolution. However, O-2 evolution was rapidly suppressed in MV-treated plants. Transcriptomic meta-analysis indicated that MDS gene expression is linked to hypoxic response not only under MV, but also in standard growth conditions. This article is part of the theme issue 'Retrograde signalling from endosymbiotic organelles'.Peer reviewe

Towards automated analysis of grain spikes in greenhouse images using neural network approaches: a comparative investigation of six methods

Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants

High-throughput non-destructive phenotyping of traits that contribute to salinity tolerance in Arabidopsis thaliana

Reproducible and efficient high-throughput phenotyping approaches, combined with advances in genome sequencing, are facilitating the discovery of genes affecting plant performance. Salinity tolerance is a desirable trait that can be achieved through breeding, where most have aimed at selecting for plants that perform effective ion exclusion from the shoots. To determine overall plant performance under salt stress, it is helpful to investigate several plant traits collectively in one experimental setup. Hence, we developed a quantitative phenotyping protocol using a high-throughput phenotyping system, with RGB and chlorophyll fluorescence imaging, which captures the growth, morphology, color and photosynthetic performance of Arabidopsis thaliana plants in response to salt stress. We optimized our salt treatment by controlling the soil-water content prior to introducing salt stress. We investigated these traits over time in two accessions in soil at 150, 100 or 50 mM NaCl to find that the plants subjected to 100 mM NaCl showed the most prominent responses in the absence of symptoms of severe stress. In these plants, salt stress induced significant changes in rosette area and morphology, but less prominent changes in rosette coloring and photosystem II efficiency. Clustering of chlorophyll fluorescence traits with plant growth of nine accessions maintained at 100 mM NaCl revealed that in the early stage of salt stress, salinity tolerance correlated with non-photochemical quenching processes and during the later stage, plant performance correlated with quantum yield. This integrative approach allows the simultaneous analysis of several phenotypic traits. In combination with various genetic resources, the phenotyping protocol described here is expected to increase our understanding of plant performance and stress responses, ultimately identifying genes that improve plant performance in salt stress conditions

TraitCapture: genomic and environment modelling of plant phenomic data

Agriculture requires a second green revolution to provide increased food, fodder, fiber, fuel and soil fertility for a growing population while being more resilient to extreme weather on finite land, water, and nutrient resources. Advances in phenomics, genomics and environmental control/sensing can now be used to directly select yield and resilience traits from large collections of germplasm if software can integrate among the technologies. Traits could be Captured throughout development and across environments from multi-dimensional phenotypes, by applying Genome Wide Association Studies (GWAS) to identify causal genes and background variation and functional structural plant models (FSPMs) to predict plant growth and reproduction in target environments. TraitCapture should be applicable to both controlled and field environments and would allow breeders to simulate regional variety trials to pre-select for increased productivity under challenging environments