Ensuring global food security amid mounting challenges, such as population growth, disease infestations, resource limitations, and climate change, is a pressing concern. Anticipated increases in food demand add further complexity to this critical issue. Plant pathogens, responsible for substantial crop losses (up to 41%) in major crops like wheat, rice, maize, soybean, and potato, exacerbate the situation. Timely disease detection is crucial, yet current practices often identify diseases at advanced stages, leading to severe infestations. To address this, remote sensing and Hyperspectral imaging (HSI) have emerged as robust and nondestructive techniques, exhibiting promising results in early disease identification. Integrating machine learning algorithms with image data sets enables precise spatial–temporal disease identification, facilitating timely detection, predictive modeling, and effective disease management without compromising fitness or climate adaptability. By harnessing these cutting-edge technologies and data-driven decision-making, growers can optimize input costs while achieving enhanced yields, making significant strides toward global food security in the face of climate change risks. This review will discuss some of the foundational concepts of remote sensing, several platforms used for remote sensing data collection, successful application of the approach, and its future perspective.

Ear rots occur globally in maize causing reduction in yield and quality of grains. In recent years, Pakistan is facing an increased mycotoxin contamination in maize caused mainly by Aspergillus spp...

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.

Albizia, is a large and complicated genus in forest plantations all around the world and closely related to Acacia. In July 2017, 65 trees of Albizia lebbeck selected from each district, out of tot...

Blackleg caused by a hemi-biotrophic fungus Plenodomus lingam (syn. Leptosphaeria maculans) poses a significant threat to global canola production. Changing climatic conditions further exacerbate the intensity and prevalence of blackleg epidemics. Shifts in temperature, humidity, and precipitation patterns can enhance pathogen virulence and disease spread. This review synthesizes the knowledge on integrated disease management (IDM) approaches for blackleg, including crop rotation, resistant cultivars, and chemical and biological controls, with an emphasis on advanced strategies such as disease forecasting models, remote sensing, and climate-adapted breeding. Notably, bibliometric analysis reveals an increasing research focus on the intersection of blackleg, climate change, and sustainable disease management. However, critical research gaps remain, which include the lack of region-specific forecasting models, the limited availability of effective biological control agents, and underexplored socio-economic factors limiting farmer adoption of IDM. Additionally, the review identifies an urgent need for policy support and investment in breeding programs using emerging tools like AI-driven decision support systems, CRISPR/Cas9, and gene stacking to optimize fungicide use and resistance deployment. Overall, this review highlights the importance of coordinated, multidisciplinary efforts, integrating plant pathology, breeding, climate modeling, and socio-economic analysis to develop climate-resilient, locally adapted, and economically viable IDM strategies for sustainable canola production.

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.