Himanshu Saini

Main conclusion This review concludes that integrating AI with smart sensing has the potential to substantially enhance hydroponic production through precise monitoring, adaptive control, and improved resource-use efficiency. Abstract Hydroponics, a key approach in controlled-environment agriculture (CEA), enables precise regulation of plant growth conditions, improving resource efficiency and year-round crop production. Recent advances in artificial intelligence (AI) and sensor technologies offer opportunities to enhance nutrient management, monitor plant health, and predict physiological responses in real time. This review critically evaluates AI-enabled hydroponic systems, focusing on sensor-driven monitoring of solution chemistry (EC, pH, DO), environmental parameters, and plant-health indicators through imaging. We highlight the application of time-series forecasting models, computer vision, and reinforcement learning for adaptive control, emphasizing their impact on plant growth, nutrient uptake, and stress mitigation. Key challenges, including sensor drift, model generalization across crops, and adoption barriers, are discussed. Finally, we outline strategies for integrating AI with plant-centered hydroponic design, including hybrid control frameworks, prototype concepts for explainable AI dashboards, and open datasets. The dashboard concept is presented as a future design direction to improve transparency and operator trust rather than as a validated user interface evaluated in this review. The accompanying conceptual implementation example illustrates how the reviewed AI methodologies could be integrated within an AIoT-enabled hydroponic system, while quantitative validation across multiple crops, cultivation cycles, and production environments remains necessary before generalizing their effectiveness.

Precision genome editing, particularly using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR‐associated protein 9 (Cas9), is advancing crop improvement by enabling targeted and efficient genetic modifications. Root and tuber crops such as potato, cassava, sweet potato, and yam are vital for global food and nutritional security but remain highly vulnerable to climate change, pests, diseases, and limited genetic diversity. Genome editing technologies facilitate the development of improved traits, including enhanced disease resistance, tolerance to abiotic stress, improved nutritional quality, and extended shelf life. This review synthesizes recent advances in genome editing for root and tuber crops across global production systems, including illustrative examples from Sub‐Saharan Africa, where active genome editing initiatives are being implemented. It further examines key technical constraints, such as low efficiency of plant transformation and regeneration, and highlights regulatory challenges arising from differing policy frameworks across countries. Emerging solutions are discussed, including genotype‐independent editing strategies and DNA‐free approaches that avoid the integration of foreign genetic material. Addressing these challenges will be critical for developing resilient and sustainable food systems. Unlike previous reviews, this study integrates mechanistic insights with cross‐crop synthesis and proposes next‐generation genome editing strategies for engineering complex traits in polyploid root and tuber crops.

Roots are pivotal for plant acclimation to environmental challenges, serving as dynamic interfaces for water and nutrient acquisition, signal integration and stress resilience. In nature and agriculture, plants are rarely exposed to single stresses in isolation; instead, they encounter multifactorial constraints such as drought × salinity, heat × nutrient limitation or sequential flooding and drought. These combinations often produce synergistic, antagonistic or neutral interactions that cannot be inferred from single‐stress studies. This review synthesizes methodological advances that enable the study of root responses beyond reductionist paradigms. We first discuss growth and performance assays that quantify root architecture, resource uptake and hydraulic function under combined stresses. We then highlight targeted molecular assays and high‐resolution omics technologies that reveal stress‐specific biochemical and regulatory signatures. Imaging methodologies, ranging from X‐ray tomography and MRI to confocal and synchrotron‐based approaches, provide spatiotemporal access to root structural and functional dynamics. Finally, we propose integrative frameworks that merge phenotyping, omics and imaging with computational modelling to disentangle the logic of root acclimation under multifactorial conditions. By bridging methodological layers, this review provides a roadmap for advancing plant stress biology toward predictive and translational frameworks, with direct implications for breeding resilient crops in the context of climate change.

CRISPR/Cas‐based genome editing has emerged as a transformative tool for precise genetic improvement of cereal crops. Recent advances in CRISPR technologies, including Cas9, Cas12, Cas13, base editing, and prime editing, have enabled targeted modification of genes and regulatory elements controlling yield, stress tolerance, and grain nutritional quality in major cereals such as rice, wheat, maize, and barley. This review summarizes current progress in CRISPR‐mediated genome editing systems, delivery strategies, and representative applications in cereal crop improvement. Emphasis is placed on how genome editing reprograms enzymatic activities and biological pathways underlying complex agronomic traits rather than acting through single‐gene effects. The review also discusses challenges related to trait complexity, regulatory considerations, and prospects for translating genome‐edited cereal crops from laboratory research to field‐level application. Collectively, this review highlights the potential of CRISPR/Cas genome editing as a powerful approach for developing high‐yielding, resilient, and nutritionally improved cereal crops.

Medicinal and aromatic plants (MAPs) produce a remarkable diversity of specialized metabolites with significant pharmaceutical, nutraceutical, and industrial value. Although advances in long‐read sequencing, chromosome‐scale genome assembly, and pangenomics have greatly expanded genomic resources, the mechanistic links between genome architecture and phytochemical diversity remain incompletely understood. The present review synthesizes current evidence describing how structural genomic variation may contribute to phytochemical diversity, while acknowledging that many proposed genome‐to‐metabolite relationships require further experimental validation. Examples illustrate how genome architecture is associated with specialized‐metabolite biosynthesis through multiple regulatory processes. However, the strength of supporting evidence varies considerably among MAP species. Moreover, relatively few genome‐to‐metabolite relationships have been confirmed through direct functional validation. We further discuss how pangenomics, multiomics integration, genome editing, synthetic biology, and artificial intelligence support the discovery, validation, and engineering of specialized metabolic pathways. Casual conclusions are evaluated according to the strength of available evidence, highlighting where causal relationships have been experimentally established and where conclusions remain primarily association‐based. Overall, this review provides an integrated conceptual and evidence‐based perspective summarizing proposed relationships between genome architecture and phytochemical diversity and outlines future priorities for functional genomics, precision breeding, metabolic engineering, and sustainable utilization of MAPs.

Trees outside forests (TOFs) represent an important natural and anthropogenic resource that contributes significantly to biodiversity conservation, climate resilience, and the protection of forest genetic resources (FGRs). Despite their ecological and socioeconomic importance, TOFs are increasingly declining due to land‐use changes, urbanization, industrial expansion, and climate change. This study aims to (i) synthesize the global evolution and current status of TOFs, (ii) critically review existing approaches for their mapping and assessment, and (iii) propose a robust framework to improve their monitoring and integration into forest management systems. A comprehensive review of literature and National Forest Inventory practices was conducted to evaluate methodologies used for TOF assessment across multiple spatial scales, ranging from national to farm level. The study examines conventional and advanced geospatial techniques, including remote sensing, high‐resolution satellite imagery, and light detection and ranging (LiDAR), highlighting their applications, strengths, and limitations. Particular attention is given to spatial mapping resolutions (1:10,000–1:50,000) and the integration of species, structural, and functional attributes into TOF assessment frameworks. The findings reveal that, despite technological advancements, no universally reliable and standardized method exists for accurately mapping TOFs, primarily due to their dispersed nature and variability across landscapes. To address these challenges, this study proposes a conceptual framework that integrates multitemporal datasets from Sentinel‐1 and Sentinel‐2 with a convolutional neural network (CNN)–based analysis to improve the detection, characterization, and spatial assessment of TOFs. The proposed framework offers a scalable and cost‐effective approach for enhancing mapping accuracy and consistency, with significant implications for National Forest Monitoring Systems, carbon stock estimation, and policy formulation. By strengthening the inclusion of TOFs in forest inventories, this work supports sustainable resource management, improved ecosystem services, and informed decision‐making for climate change mitigation and biodiversity conservation.