Manoj Kumar Mishra

The increasing limitations of chemical pesticides such as environmental pollution, pathogen resistance, and threats to human and ecosystem health have increased the demand for sustainable, biologically based crop protection methods. Eco‐smart biocontrol has emerged as a game‐changing paradigm that uses beneficial microorganisms associated with plants to suppress phytopathogens, boost plant immunity, and make agroecosystems more resilient over time. Moving beyond traditional single‐strain biocontrol, eco‐smart biocontrol integrates multi‐omics discovery, artificial intelligence‐assisted predictive microbiome design, and dynamic rhizosphere ecology. This review brings together ecological, molecular, and technological dimensions of eco‐smart biocontrol, focusing on the rhizosphere as a dynamic hotspot for plant–microbe interactions. We investigate rhizosphere microbiome assembly and demonstrate the preferential recruitment of beneficial bacteria, fungi, actinomycetes, and mycorrhizal symbionts by plant root exudates. Moreover, the review highlights the impact of innovations in multi‐omics techniques (metagenomics, transcriptomics, proteomics, and metabolomics), systems biology, and artificial intelligence on microbial biocontrol agent discovery, functional validation, and predictive design. Examples from cereal crops, legumes, and horticulture crops indicate that the application of beneficial microbial inoculants can significantly lower the burden of pests and diseases, enhance crop productivity, and fit perfectly within an integrated pest management system. Lastly, we critically analyze the main challenges preventing large‐scale adoption, such as inconsistent field performance, limited microbial survival and competitiveness, and comparative regulatory frameworks across global markets. Ultimately, eco‐smart microbial biocontrol combines mechanistic insights with omics‐driven discovery, artificial intelligence (AI)‐ assisted prediction, advanced formulation strategies, and field‐level validation, creating a strong, scalable, and environmentally friendly framework for resilient, low‐input agricultural systems.

Major environmental pollutants include heavy metals including cadmium, copper, lead, chromium, and mercury. These pollutants are especially prevalent in places with significant anthropogenic pressure. Because heavy metal buildup in soils can have detrimental impacts on crop growth caused by phytotoxicity, food safety and marketability, and the environmental health of soil organisms, it is a major concern in agricultural production. Through contamination of the air, water, and soil, plants' influence and metabolic processes have an impact on the biological and geological redistribution of heavy metals. Excessive reactive oxygen species (ROS) and methylglyoxal (MG) accumulation is a typical side effect of heavy metal toxicity. ROS and MG can both induce lipid peroxidation, protein oxidation, enzyme inactivation, DNA damage, and/or interact with other essential components of plant cells. This review focuses on effect of heavy metals on plant growth, yield and their mode of toxic effects in plants.

Resveratrol, a naturally occurring polyphenol, exhibits anticancer, anti-inflammatory, and antioxidant properties. However, its molecular mechanisms in pancreatic cancer remain incompletely understood, necessitating integrative approaches such as network pharmacology and molecular docking. Potential resveratrol targets were identified using Swiss Target Prediction, while pancreatic cancer–related genes were retrieved from GeneCards and NCBI Gene databases. Overlapping targets were obtained through Venn analysis, followed by protein– protein interaction network construction, hub gene selection, and enrichment analysis. Molecular docking validated compound–target interactions. A total of 100 predicted resveratrol targets and 1,447 pancreatic cancer– associated genes were screened, yielding 39 overlapping genes. Network analysis identified hub genes including EGFR, SRC, MTOR, PIK3CA, PIK3CB, BCL2, and PTGS2. Gene Ontology enrichment indicated roles in cell proliferation, apoptosis regulation, inflammatory response and metabolic regulation while KEGG pathway analysis highlighted the PI3K–Akt, ErbB, and EGFR inhibitor resistance signaling as being closely associated with pancreatic cancer pathway. Docking analysis revealed strong binding of resveratrol with KRAS (–8.2 kcal/mol), EGFR (–7.9 kcal/mol), and MTOR (–7.7 kcal/mol), stabilized by hydrogen bonding. The interaction with KRAS, although not among the predicted targets. Expression profiling validated upregulation of hub genes in tumor samples. Resveratrol exerts multi-targeted effects in pancreatic cancer by modulating oncogenic pathways, particularly KRAS and PI3K/Akt/mTOR signaling. Its favourable safety profile and robust hub gene interactions highlight its potential as a supplementary therapeutic drug, necessitating further preclinical and clinical confirmation.

Plant viral infections are a major concern in worldwide agriculture, resulting in considerable economic losses and jeopardizing food security. Conventional management measures, such as chemical vector control and resistance breeding, frequently fail due to changing virus strains and environmental issues. This review synthesizes recent advances in nanoparticle-based approaches for managing plant viral diseases, emphasizing mechanistic action, application strategies, and regulatory challenges. Four major antiviral mechanisms were identified: direct viral inactivation, inhibition of vector transmission, induction of systemic resistance, and nanocarrier-mediated RNAi or CRISPR delivery. Silver and gold nanoparticles exhibited strong virucidal activity, while silica and zinc nanoparticles primarily enhanced systemic acquired resistance through salicylic acid signaling. Green synthesis methods improved nanoparticle stability, biocompatibility, and environmental safety compared to conventional chemical synthesis. Integration of nanoparticles within integrated pest management (IPM) frameworks enhanced virus detection, vector suppression, and targeted delivery, minimizing reliance on agrochemicals. However, field-scale deployment remains limited by production cost, stability, and lack of harmonized regulatory guidelines across regions such as the EU, US, and India. Overall, this review provides a mechanistic and translational framework for developing sustainable, field-ready nanoparticle technologies for effective plant viral disease management.

Environmental monitoring is essential for understanding and minimizing human impact on ecosystems. Traditional methods like manual sampling and laboratory testing, while accurate, are often costly, time-consuming, and difficult to scale, especially in low-resource settings. Artificial intelligence (AI) is increasingly addressing these limitations by enabling automated data collection, real-time analysis, and predictive modeling. Techniques such as machine learning (ML) and deep learning (DL) are being applied to monitor air and water quality, track climate patterns, and support biodiversity efforts. Hybrid AI models further improve accuracy by integrating various analytical approaches. Key applications include species identification, habitat assessment, wildlife tracking, and anti-poaching, utilizing tools such as drone imagery, camera traps, and GPS data. This review explores the latest advancements in AI-based environmental monitoring, emphasizing technologies like explainable AI (XAI), edge computing, and the Internet of Things (IoT), which improve transparency and reduce processing costs. It also addresses ongoing challenges, including data quality, computational demands, and the need for interpretable models. By evaluating practical limitations and proposing interdisciplinary strategies, this article highlights the transformative potential of AI for sustainable environmental management. Successful implementation will depend on ethical frameworks, policy alignment, and cross-sector collaboration to fully realize AI’s role in global ecological stewardship.