Drought and excess ambient temperature intensify abiotic and biotic stresses on agriculture, threatening food security and economic stability. The development of climate-resilient crops is crucial for sustainable, efficient farming. This review highlights the role of multi-omics encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics in identifying genetic pathways for stress resilience. Advanced phenomics, using drones and hyperspectral imaging, can accelerate breeding programs by enabling high-throughput trait monitoring. Artificial intelligence (AI) and machine learning (ML) enhance these efforts by analyzing large-scale omics and phenotypic data, predicting stress tolerance traits, and optimizing breeding strategies. Additionally, plant-associated microbiomes contribute to stress tolerance and soil health through bioinoculants and synthetic microbial communities. Beyond agriculture, these advancements have broad societal, economic, and educational impacts. Climate-resilient crops can enhance food security, reduce hunger, and support vulnerable regions. AI-driven tools and precision agriculture empower farmers, improving livelihoods and equitable technology access. Educating teachers, students, and future generations fosters awareness and equips them to address climate challenges. Economically, these innovations reduce financial risks, stabilize markets, and promote long-term agricultural sustainability. These cutting-edge approaches can transform agriculture by integrating AI, multi-omics, and advanced phenotyping, ensuring a resilient and sustainable global food system amid climate change.

Rhizosphere microbiome exerts a significant role in plant health, influencing nutrient availability, disease resistance, and overall plant growth. Establishing a robust and efficient nodulation process is essential for optimal nitrogen fixation in legumes like soybeans. Different soybean genotypes exhibit variations in their rhizosphere microbiome, potentially impacting nitrogen fixation through nodulation. However, a detailed understanding of how specific soybean genotypes influence rhizosphere microbial communities and nodulation patterns remains limited. Our study aims to investigate the relationship between rhizosphere microbial abundance and plant growth in four soybean genotypes. We evaluated plant growth parameters, including biomass, leaf area, and stomatal conductance, and identified significant genotypic differences in nodulation. Specifically, genotypes PI 458505 and PI 603490 exhibited high levels of nodulation, while PI 605839A and PI 548400 displayed low nodulation. 16S rRNA gene amplicon sequencing revealed diverse bacterial communities in the rhizosphere, with Proteobacteria as the dominant phylum. High-nodulation genotypes harbored more diverse microbial communities enriched with Actinobacteria and Acidobacteriota, while low-nodulation genotypes showed higher abundances of Firmicutes and Planctomycetota. Alpha and beta diversity analyses confirmed distinct microbial community structures between high- and low-nodulation groups. Our findings suggest that the rhizosphere microbiome significantly influences soybean growth and nodulation, highlighting the potential for genotype-driven strategies to enhance plant-microbe interactions and improve soybean productivity.

Drought and excess ambient temperature intensify abiotic and biotic stresses on agriculture, threatening food security and economic stability. The development of climate-resilient crops is crucial for sustainable, efficient farming. This review highlights the role of multi-omics encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics in identifying genetic pathways for stress resilience. Advanced phenomics, using drones and hyperspectral imaging, can accelerate breeding programs by enabling high-throughput trait monitoring. Artificial intelligence (AI) and machine learning (ML) enhance these efforts by analyzing large-scale omics and phenotypic data, predicting stress tolerance traits, and optimizing breeding strategies. Additionally, plant-associated microbiomes contribute to stress tolerance and soil health through bioinoculants and synthetic microbial communities. Beyond agriculture, these advancements have broad societal, economic, and educational impacts. Climate-resilient crops can enhance food security, reduce hunger, and support vulnerable regions. AI-driven tools and precision agriculture empower farmers, improving livelihoods and equitable technology access. Educating teachers, students, and future generations fosters awareness and equips them to address climate challenges. Economically, these innovations reduce financial risks, stabilize markets, and promote long-term agricultural sustainability. These cutting-edge approaches can transform agriculture by integrating AI, multi-omics, and advanced phenotyping, ensuring a resilient and sustainable global food system amid climate change.

Submerged plants can thrive entirely underwater, playing a crucial role in maintaining water quality, supporting aquatic organisms, and enhancing sediment stability. However, they face multiple challenges, including reduced light availability, fluctuating water conditions, and limited nutrient access. Despite these stresses, submerged plants demonstrate remarkable resilience through physiological and biochemical adaptations. Additionally, their interactions with microbial communities are increasingly recognized as pivotal in mitigating these environmental stresses. Understanding the diversity of these microbial communities is crucial for comprehending the complex interactions between submerged plants and their environments. This research aims to identify and screen microbes from submerged plant samples capable of producing 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase and to explore microbial diversity through metagenomic analysis. Microbes were isolated and screened for ACC deaminase production, and metagenomic techniques, including co-occurrence network analysis, were used to examine microbial diversity and interactions within the communities. ACC deaminase-producing microbes can significantly enhance plant metabolism under stress conditions. The identification of the culturable bacteria revealed that most of these microbes belong to the genera Pseudomonas, Bacillus, and Acinetobacter. A total of 177 microbial strains were cultured, with molecular identification revealing 79 reductant, 86 non-reductant, and 12 uncultured strains. Among 162 samples screened for ACC deaminase activity, 50 tested positive. To further understand microbial dynamics, samples were collected from both natural sources and artificial pond reservoirs to assess the impact of the location on flood-associated microbiomes in submerged plants. Metagenomic analysis was conducted on both the epiphytic and endophytic samples. By exploring the overall composition and dynamics of microbial communities associated with submerged plants, this research seeks to deepen our understanding of plant–microbe interactions in aquatic environments. The microbial screening helped to identify the diverse microbes associated with ACC deaminase activity in submerged plants and amplicon sequencing analysis paved the way towards identifying the impact of the location in shaping the microbiome and the diversity associated with endophytic and epiphytic microbes. Co-occurrence network analysis further highlighted the intricate interactions within these microbial communities. Notably, ACC deaminase activity was observed in plant-associated microbes across different locations, with distinct variations between epiphytic and endophytic populations as identified through co-occurrence network analysis.

Plasma treatment has emerged as a promising tool for manipulating plant microbiomes and metabolites. This review explores the diverse applications and effects of plasma on these biological systems. It is hypothesized that plasma treatment will not induce substantial changes in the composition of plant microbiomes or the concentration of plant metabolites. We delve into the mechanisms by which plasma can regulate microbial communities, enhance antimicrobial activity, and recruit beneficial microbes to mitigate stress. Furthermore, we discuss the optimization of plasma parameters for effective microbiome interaction and the role of plasmids in plant–microbe interactions. By characterizing plasmidome responses to plasma exposure and investigating transcriptional and metabolomic shifts, we provide insights into the potential of plasma as a tool for engineering beneficial plant–microbe interactions. The review presented herein demonstrates that plasma treatment induces substantial changes in both microbial community composition and metabolite levels, thereby refuting our initial hypothesis. Finally, we integrate plasmidome, transcriptome, and metabolome data to develop a comprehensive understanding of plasma’s effects on plant biology and explore future perspectives for agricultural applications.

Aquatic ecosystems are under severe stress from a diverse combination of contaminants, including heavy metals, pesticides, pharmaceuticals, and microplastics, driven by rapid industrialization, intensive agriculture, and urbanization. Globally, 80% of wastewater remains untreated, and conventional systems often fail to address emerging contaminants. Consequently, toxic heavy metals like lead and mercury can persist in water sources for decades. In response, phytoremediation has emerged as a scalable, eco-friendly, nature-based alternative. Among phytoremediation agents, duckweeds are increasingly recognized for their rapid growth, simple morphology, and continuous water-column contact. This review outlines the landscape of duckweed-based remediation, detailing molecular detoxification pathways and the synergistic role of associated microbiomes in enhancing environmental cleanup. Evidence indicates that contaminant removal is often supported by plant-microbe interactions. Despite extensive laboratory validation, field-scale implementation remains constrained by environmental complexity, pollutant mixtures, and variable climatic conditions. Furthermore, while duckweed systems hold promise within circular bioeconomy frameworks, converting wastewater into nutrient-rich biomass, contaminant accumulation in plant tissues raises concerns about biomass utilization and contaminant carryover. Addressing these challenges requires an integrative approach that links molecular detoxification, ecological interactions, and engineered system design to realize the full potential of duckweeds for sustainable aquatic pollution management.