Image-based Analysis to Study Plant Infection with Human Pathogens

Estimation model of winter wheat disease based on meteorological factors and spectral information

Abstract

Wheat scab (WS, Fusarium head blight), one of the most severe diseases of winter wheat in Yangtze-Huaihe river region, whose monitoring and timely forecasting at large scale would help to optimize pesticide spraying and achieve the purpose of reducing yield loss. In the present study, remote sensing monitoring on WS was conducted in 4 counties in Yangtze-Huaihe river region. Sensitive factors of WS were selected to establish the remote sensing estimation model of winter wheat scab index (WSI) based on interactions between spectral information and meteorological factors. The results showed that: 1) Correlations between the daily average temperature (DAT) and daily average relative humidity (DAH) at different time scales and WSI were significant. 2) There were positive linear correlations between winter wheat biomass, leaf area index (LAI), leaf chlorophyll content (LCC) and WSI. 3) NDVI (normalized difference vegetation index), RVI (ratio vegetation index) and DVI (difference vegetation index) which had a good correlation with LAI, biomass and LCC, respectively, and could be used to replace them in modeling. 4) The estimated values of the model were consistent with the measured values (RMSE = 5.3%, estimation accuracy = 90.46%). Estimation results showed that the model could efficiently estimate WS in Yangtze-Huaihe river region.

Graphical abstract

Long-Term Warming Shifts the Composition of Bacterial Communities in the Phyllosphere of Galium album in a Permanent Grassland Field-Experiment

Global warming is currently a much discussed topic with as yet largely unexplored consequences for agro-ecosystems. Little is known about the warming effect on the bacterial microbiota inhabiting the plant surface (phyllosphere), which can have a strong impact on plant growth and health, as well as on plant diseases and colonization by human pathogens. The aim of this study was to investigate the effect of moderate surface warming on the diversity and composition of the bacterial leaf microbiota of the herbaceous plant Galium album. Leaves were collected from four control and four surface warmed (+2°C) plots located at the field site of the Environmental Monitoring and Climate Impact Research Station Linden in Germany over a 6-year period. Warming had no effect on the concentration of total number of cells attached to the leaf surface as counted by Sybr Green I staining after detachment, but changes in the diversity and phylogenetic composition of the bacterial leaf microbiota analyzed by bacterial 16S rRNA gene Illumina amplicon sequencing were observed. The bacterial phyllosphere microbiota were dominated by Proteobacteria, Bacteroidetes, and Actinobacteria. Warming caused a significant higher relative abundance of members of the Gammaproteobacteria, Actinobacteria, and Firmicutes, and a lower relative abundance of members of the Alphaproteobacteria and Bacteroidetes. Plant beneficial bacteria like Sphingomonas spp. and Rhizobium spp. occurred in significantly lower relative abundance in leaf samples of warmed plots. In contrast, several members of the Enterobacteriaceae, especially Enterobacter and Erwinia, and other potential plant or human pathogenic genera such as Acinetobacter and insect-associated Buchnera and Wolbachia spp. occurred in higher relative abundances in the phyllosphere samples from warmed plots. This study showed for the first time the long-term impact of moderate (+2°C) surface warming on the phyllosphere microbiota on plants. A reduction of beneficial bacteria and an enhancement of potential pathogenic bacteria in the phyllosphere of plants may indicate that this aspect of the ecosystem which has been largely neglected up till now, can be a potential risk for pathogen transmission in agro-ecosystems in the near future.

Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

BACKGROUND

Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance.

RESULTS

In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study.

CONCLUSIONS

Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.