Artificial intelligence (AI) and big data have greatly transformed plant stress management practices in smart agriculture. Conventional stress management approaches have many limitations regarding precision, efficiency, and scalability, which are being resolved using modern digital technologies. Machine learning (ML) and deep learning are advanced AI techniques that can easily analyze diverse data from images, sensors, and multi-omics. These assist in early stress identification, classification, and prediction of future risks associated with stress. ML models (both supervised and unsupervised) have the ability to accurately classify the stress types. Computer vision technology is widely used in agriculture and helps in conducting timely decisions by detecting morphological and spectral alterations in plants. Big data facilitates unraveling complex mechanisms underlying plant stress through integrative multi-omics. Various AI-driven predictive models have also been applied in forecasting insect pests and disease outbreaks. Furthermore, AI and big data are revolutionizing agriculture through precision fertilization and irrigation, robotic spraying technology and AI-integrated farm management information systems. Thus, AI and big data have significantly advanced conventional methods in plant stress management.

Management of plant viruses can succeed with a stacked, diversified resistance strategy. This chapter combines non-transgenic and engineered techniques into a useful, field-based paradigm. We begin by examining non-transgenic approaches that distribute selection pressure across several loci, and induce resistance (SAR/ISR), emphasizing how these layers influence infection, titer, and epidemic progression within the confines of actual cropping conditions. Next, we look at transgenic and genome-engineering methods, such as post-transcriptional gene silencing (RNAi/PTGS) and transcriptional gene silencing (TGS/RdDM) for DNA viruses. These methods include design rules for multiplex targeting and promoter methylation to make it harder for viruses to escape; CRISPR pathways that either edit host susceptibility factors or damages directly the viral genome; and protein-mediated options (coat-protein/replicase constructs, plantibodies, and new receptor engineering). We combine host features that make it harder for vectors to spread – with selective management of insect and behavioral interference on particular landscape. Recent developments in the host were reviewed including multilayer stacking and SIGS with details of carriers for stability enhancement. We stress stewardship, risk of resistance erosion, region-specific adoption paths for smallholder and commercial systems, and outcome indicators (incidence, viral load, yield) throughout. When taken as a whole, the chapter offers decision-making resources and cutting-edge examples for creating reliable, escape-resistant programs that convert lab advancements into solid field performance.

Flaviviruses such as dengue virus (DENV) and Zika virus (ZIKV) co-circulate widely and cause significant morbidity, yet effective broad-spectrum antivirals are limited. This study evaluated the antiviral efficacy, cytotoxicity, and host transcriptional responses to the nucleic acid–hydrolyzing antibody fragment 3D8 scFv in mono- and co-infection models. RNA sequencing of A549 cells treated with 3D8 scFv revealed a dose-dependent activation of the MAPK–HSP70 stress response, with minimal transcriptomic disruption at antiviral concentrations. Comparative transcriptomic analysis identified distinct host signatures for ZIKV and DENV2, and machine learning classifiers accurately distinguished infection states (AUC > 0.95). In Vero E6 cells, prophylactic treatment with 3D8 scFv significantly reduced viral RNA, protein expression, and infectious particle production for both viruses, including during co-infection. Optimized post-entry treatment also demonstrated antiviral activity. Cytotoxicity assays confirmed good tolerability at effective concentrations. These findings indicate that 3D8 scFv inhibits viral replication through early cleavage of viral nucleic acids while inducing a limited protective stress response, supporting its development as a broad-spectrum antiviral candidate.

Medicago sativa commonly known as alfalfa, is a highly cultivated forage crop that produces many secondary metabolites (SMs) such as saponins, alkaloids and phenolic compounds. These metabolites play a fundamental role in plant defense mechanisms against biotic stresses, particularly viral infections. Alfalfa is vulnerable to various DNA and RNA viruses that compromise its production. SMs act as an antiviral agent by preventing viral entry, prohibiting their replication and movement in plant cells. These also improve plant's immunity and confer systemic acquired resistance. The biosynthesis of SMs in alfalfa is controlled by several metabolic pathways and transcription factors such as WRKY, MYB, bHLH, bZIP, AP2/ERF, and NAC. Additionally, SMs enhance the efficacy of biofertilizers by developing favorable microbial environment within the rhizosphere. Metabolic engineering techniques such as overexpression of targeted pathways, prevention of rate-limiting steps, and hindering competing pathways can optimize the production and efficiency of these SMs. Research should focus on exploiting the potential of SMs in sustainable agriculture to boost crop yield, resilience and reduce reliance on chemical-based fertilizers.

Arbuscular mycorrhizal fungi (AMF) are transitioning toward scalable biostimulants despite various challenges. This chapter redefines AMF production using soil-less, sterile pipelines that initiate with soil-free root organ culture (ROC: transformed roots used for soil-less contaminant-controlled propagation) and advance to low-shear, O₂-enriched bioreactors designed for process regulation and harvesting. In this study, recent advancements in encapsulation and stabilization, specifically alginate beads, film coating, and cryopreservation, are discussed to facilitate dosage precision, moisture regulation, extended shelf life, and traceability via master bank systems. A realistic standardized quality framework is proposed that assesses identity and purity, viability, and function through infective propagule (IP) bioassays. The soil free culture was synthesized on the transformed roots in the customized bioreactor that enabled the propagation of propagules under the aseptic conditions. This helps in improving productivity and standardizing quality. We also emphasized the cryopreservation and encapsulation strategies to explain the role of alginate and other biodegradable carriers in maintaining viability. It can ensure the storage and stability of strains for a longer duration. We discussed the principles of engineering of formulations along with molecular methods. We linked these components and design principles for AMF and mycorrhiza-helper-bacterial consortia. However, in the techno-economic analysis, some cost determinants were identified. We emphasized uniform measurements to ensure that the performance assertions are reproducible across various crops and locations. It is also discussed to enhance the role of beneficial microbes to be used as co-inoculants. It can increase their efficiency in field particularlry in the abiotic conditions. The result of this chapter suggests bridging the gap in the lbal research and economical field formulations.