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Background: Disease suppressiveness of soils to fungal root pathogens is typically induced in the field by repeated infections of the host plant and concomitant changes in the taxonomic composition and functional traits of the rhizosphere microbiome. Here, we studied this remarkable phenomenon for Bipolaris sorokiniana in two wheat cultivars differing in resistance to this fungal root pathogen. Results: The results showed that repeated exposure of the susceptible wheat cultivar to the pathogen led to a significant reduction in disease severity after five successive growth cycles. Surprisingly, the resistant wheat cultivar, initially included as a control, showed the opposite pattern with an increase in disease severity after repeated pathogen exposure. Amplicon analyses revealed that the bacterial families Chitinophagaceae, Anaerolineaceae and Nitrosomonadaceae were associated with disease suppressiveness in the susceptible wheat cultivar; disease suppressiveness in the resistant wheat cultivar was also associated with Chitinophagaceae and a higher abundance of Comamonadaceae. Metagenome analysis led to the selection of 604 Biosynthetic Gene Clusters (BGCs), out of a total of 2,571 identified by AntiSMASH analysis, that were overrepresented when the soil entered the disease suppressive state. These BGCs are involved in the biosynthesis of terpenes, non-ribosomal peptides, polyketides, aryl polyenes and post-translationally modified peptides. Conclusion: Combining taxonomic and functional profiling we identified key changes in the rhizosphere microbiome during disease suppression. This illustrates how the host plant relies on the rhizosphere microbiome as the first line of defense to fight soil-borne pathogens. Microbial taxa and functions identified here can be used in novel strategies to control soil-borne fungal pathogens. [ABSTRACT FROM AUTHOR]

• An automatic method for counting, classification and measurement of Rhopalosiphum padi , an economically important aphid species. • A new software to apply our method is presented: AphidCV. • A validation study show very good results in counting, classification and measurement of aphids, specially nymph and wingless classes. • The biggest gain in the use of AphidCV is undoubtedly the time it takes to perform the laboratory analyzes. • AphidCV allow future integration with new and third party systems, and support to include new aphid species. Aphids are insects that attack crops and cause damage directly, by consuming the sap of plants, and indirectly, by vectoring microorganisms that can cause diseases. Cereal crops are hosts for many aphid species, including Rhopalosiphum padi (an economically important aphid species). Recording and classifying aphids are necessary for evaluating and predicting crop damage. Thus, serving as a basis for decision making on the utilization of control measures. It can also be useful to evaluate plant resistance to aphids. Traditionally, the recording process is manual and depends on magnification and well-trained staff. The manual counting is also a time-consuming process and susceptible to errors. With this in mind, this paper presents a method and software to automate the counting and classification of Rhopalosiphum padi using image processing, computer vision, and machine learning methods. The text also presents a comparison of manually counts from experts and values obtained with the software, considering 40 samples. The results showed strong positive correlation in counting and classification ( r s = 0.92579) and measurement (r = 0.9799). Concluding, the software proved to be reliable and useful to aphid population monitoring studies. [ABSTRACT FROM AUTHOR]