Phytopathogens are one of the major detrimental agents which cause significant yield loss and affect crop productivity and food security. Diagnosis of phytopathogen in the initial stages of disease development is essential for accurate field-level management. The disease can be currently diagnosed early from the field more precisely because of the development of precise, quick, and sensitive technologies. This is especially true today, when variables like climate change may drive infections to arise in regions where they were unanticipated in the past. The traditional identification methods largely relied on the cultural characteristics of pathogens, the symptomatic expression of plants infected, or based on the signs present in infected plants. These methods largely involved skilled experts and it is time-consuming. On the other hand, the recent developments in DNA-based diagnosis methods have become popular in the diagnosis of phytopathogen. Advanced molecular methods such as PCR-based methods, isothermal amplification-based methods, probe-based methods, post-amplification techniques, next-generation sequencing, and approaches grounded on the analysis of volatile compounds are being used in the detection of several phytopathogen owing to their sensitivity and reliability. Further, these techniques have the advantage of their applicability to non-culturable pathogens. The current review aims to explore some of the potential methods and advances in the field of real-time detection of plant pathogens and how these methods may alter farmers and pathologists in diagnosing plant diseases.

Metagenomics is the most prominent and powerful tool for understanding the genetic variability of microorganisms communities in different complex ecosystems. In recent years, several studies have been carried out in this research field. Genome-level validation through high-throughput next-generation sequencing technologies and bioinformatics tools leads to the independent assessment of microbial communities. Application of high-throughput sequencing and metagenomics studies opened a new window into metabolic diversity in the microbial communities, phylogenetic analysis, the origin of the microorganism, expression of genes at diverse environmental conditions, recovery of novel biomolecules, and identification of new isolates. These microbes are involved in numerous biogeochemical cycles and decomposition. Integrating metagenomics with bioinformatic tools helps better monitor and understand various environmental and ecological strategies for microbial diversity, evolution, and adaptation in multiple ecosystems.

Rhizobacteria are one of the ecofriendly strategies which can be used in enhancing agriculture productivity. They are rhizosphere-inhibiting bacteria that are directly and indirectly involved in encouraging plant growth and development by producing various chemicals in the rhizosphere. Under both normal and stressful conditions, the use of plant growth–promoting bacteria has been shown to improve the health and productivity of several plant species, by producing exopolysaccharides phytohormones, 1-aminocyclopropane -1-carboxylate (ACC) deaminase, volatile compound, induced accumulation of osmolytes, antioxidant, upregulating or downregulating stress-responsive genes, and altering root morphology. These advantageous microorganisms colonize the rhizosphere and endo-rhizosphere of plants and impart drought tolerance. Additionally, these microorganisms can increase crop resistance to abiotic stress like salinity, heat, and drought. The management of a sustainable agriculture system will be made easier by recent advancements in the understanding of the diversity of rhizobacteria in the rhizosphere, their capacity to colonize and their mode of action in reducing biotic stress. In future, it may be possible and perhaps practical to use these rhizobacteria as biofertilizers and biopesticides which have least adverse effects on the environment.