Abiotic stresses, such as drought, salinity, and heat, exacerbated by climate change, pose significant challenges to global agriculture. These stresses negatively impact crop physiology, leading to yield losses and complicating efforts to breed resilient varieties. While advancements in molecular biology and genomics have identified stress-resistance genes, their effective utilization in breeding programs depends on precise phenotypic evaluation under diverse stress conditions. High-throughput phenotyping (HTP) technologies have emerged as indispensable tools, enabling non-destructive, rapid assessment of critical traits like root architecture, chlorophyll content, and canopy temperature in controlled and field environments. Unlike existing reviews, this manuscript critically addresses technological barriers such as cost scalability, field adaptability, and the integration of artificial intelligence for real-time data analysis. Additionally, it provides a fresh perspective on multi-omics integration in phenomics to bridge the genotype–phenotype gap, ensuring a more holistic approach to precision agriculture. This review bridges gaps in crop improvement by identifying practical solutions to enhance the adoption of HTP in breeding programs. It ensures food security amidst the escalating impacts of climate change.

Basal stalk rot (BSR) of cultivated sunflower ( Helianthus annuus) is caused by the necrotrophic fungal pathogen Sclerotinia sclerotiorum. This disease is economically significant and limits sunflower production in the northern Great Plains region of the United States. Resistance to BSR is quantitative, controlled by many genes exerting small effects on the level of resistance. This genetic complexity hinders efforts to develop sunflower hybrids with sufficient resistance. Field-based disease trials have successfully identified cultivated sunflower genotypes with partial BSR resistance but failed to determine the degree of resistance. Consequently, the objectives of this study were to (i) distinguish between highly and moderately resistant genotypes by reevaluating 60 cultivated sunflower genotypes exhibiting partial resistance in field trials using a newly developed greenhouse inoculation method with improved resolution; (ii) determine if selected genotypes identified in objective (i) are broadly resistant to diverse S. sclerotiorum isolates; (iii) assess potential host genotype–pathogen isolate interactions between sunflower genotypes and S. sclerotiorum isolates; and (iv) determine if resistant genotypes carry alleles of molecular markers previously associated with BSR resistance loci and assess the frequency of these alleles in resistant germplasm. The results of this study identified sunflower inbred lines HA 124 and HIR 34 exhibiting high levels of BSR resistance against all tested S. sclerotiorum isolates. Additionally, significant host genotype–pathogen isolate interactions were detected between sunflower lines and S. sclerotiorum isolates. This information will guide breeding efforts to improve BSR resistance and facilitate prioritizing highly resistant lines for genetic mapping and further characterization.

This study investigated the combining ability, heterosis and heterotic grouping of maize (Zea mays L.) inbred lines to enhance hybrid performance and productivity. Twenty-four hybrids were developed by crossing eight inbred lines with three testers, and their performance was evaluated for two years at Banaras Hindu University's agricultural research farm. Data on yield and yield-attributing traits were collected from selectively centred competitive plants in each row, avoiding border plants to reduce errors. Biometrical techniques, cluster analysis, and statistical tools were employed to measure general combining ability (GCA), specific combining ability (SCA), and standard heterosis, providing insights into hybrid performance. Analysis of variance revealed significant mean square values for GCA and SCA across most traits studied. Various methods were utilized, including SCA effects, HGCAMT (Heterosis Grouping by Combining Ability of Multiple Traits), and HSGCA (Heterotic Grouping based on Specific and General Combining Ability). The study identified HUZM-242 × CML-286 and HUZM-53 × CML-286 as crosses displaying higher grain yield compared to the check line DKC 7074 and exhibiting positive heterosis. The findings offer valuable guidance for maize breeding programmes by accurately identifying heterotic groups, enabling breeders to select inbred lines more likely to produce high-performing hybrids. This targeted selection reduces the number of necessary cross-breeding trials, saving time and resources. Additionally, hybrids derived from crosses between lines from different heterotic groups exhibit superior performance due to higher heterosis. These conclusions support advancements in maize breeding strategies, ultimately contributing to agricultural sustainability through increased productivity, resource efficiency, and economic benefits for farmers.

Millets are climate-resilient, nutritionally rich cereal crops that play a critical role in food and nutritional security across arid and semi-arid regions. Despite their inherent tolerance to harsh environments, millet productivity is increasingly challenged by combined biotic and abiotic stresses, including drought, heat, salinity, and emerging diseases. Transcription factors are central regulators of plant stress responses, and among them, WRKY transcription factors constitute one of the largest and most plant-specific families, coordinating defense and stress-adaptive pathways. WRKY transcription factors are characterized by a conserved WRKY domain and zinc finger motif that enable binding to W-box cis-elements in target gene promoters, thereby regulating stress-responsive gene expression. This review synthesizes current knowledge on the structure, classification, and functional roles of WRKY transcription factors in major millet crops, including pearl millet, finger millet, foxtail millet, and related species. We summarize recent advances in millet genomic and transcriptomic resources that have facilitated genome-wide identification of WRKY gene families and their stress-responsive expression patterns. Particular emphasis is placed on WRKY-mediated regulation of disease resistance, abiotic stress tolerance, and hormonal crosstalk involving salicylic acid, jasmonic acid, ethylene, and abscisic acid signaling. Emerging evidence highlights WRKYs as central regulatory hubs that integrate biotic and abiotic stress signals via MAPK cascades, transcription factor networks, and redox signaling. Finally, the potential of WRKY transcription factors to improve millet through molecular breeding, genome editing, and marker-assisted selection is discussed, along with key challenges and future research directions.

WRKY transcription factors are among the largest plant-specific transcription factor families and play central roles in coordinating gene expression during biotic and abiotic stress. Despite decades of research, a fundamental paradox remains: WRKY proteins bind short, widely distributed W-box cis-elements yet generate highly selective, context-dependent transcriptional outputs in vivo. In this review, we argue that WRKY specificity is not determined by DNA binding alone but emerges from the integration of five regulatory layers: cis-regulatory motif grammar, chromatin accessibility and epigenetic state, protein–protein interactions, post-translational modifications and proteostasis, and signaling context, which we collectively define as the WRKY regulatory code. We discuss how chromatin state controls which genomic W-box sites are physically accessible, while post-translational modifications and cofactor interactions determine which accessible sites are productively engaged and whether the transcriptional output is activation or repression. Representative mechanistic examples illustrate how different combinations of these regulatory layers generate precise, condition-dependent transcriptional programs. We further discuss how multi-omics integration and machine learning-based predictive modeling can decode and empirically test this regulatory code across stresses, tissues, and developmental contexts. The regulatory code perspective resolves key inconsistencies in WRKY biology and provides a practical conceptual framework for more targeted manipulation of stress-responsive transcriptional networks in crops.

Exserohilum spike blight, caused by Exserohilum rostratum, is an emerging constraint in wheat production, and improving host resistance is a sustainable strategy because the disease is governed largely by quantitative, environment-responsive loci rather than single major genes. Identifying genomic regions and biological pathways underlying quantitative resistance is therefore essential for developing durable resistant varieties. In this study, the Wheat Association Mapping Initiative spring wheat panel (n = 289) was evaluated across two contrasting Indian agroclimatic zones over two seasons. Resistance was quantified using two traits: incubation period (IP) and area under the disease progress curve (AUDPC). Significant genotype, environment, and genotype × environment interaction effects were observed for both traits (p < 0.001). IP and AUDPC were weakly correlated (r = −0.18 to 0.16), indicating that these traits represent partially distinct resistance components. Genome-wide association mapping identified 25 marker–trait associations, 13 for AUDPC and 12 for IP, with most associations showing environment dependence. Putative candidate genes highlighted defense-relevant loci, including an LRR receptor-like kinase and a WRKY transcription factor for IP, and an RGA2-like resistance gene and PHLOEM UNLOADING MODULATOR-like gene for AUDPC. These findings provide the first GWAS-based framework for E. rostratum resistance in wheat, prioritizing loci for validation and marker deployment.