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To sustain global food security under the growing pressures of population expansion, economic development, and climate change, agricultural productivity must be enhanced through sustainable and innovative approaches. Genetic crop improvement integrating advances in molecular biology, biotechnology, genomics and plant physiology offers a powerful means to achieve this goal. However, the development of superior crop varieties often encounters the challenge of trait trade-offs, where the expression of one desirable characteristic can compromise another. In this context, microRNAs (miRNAs), a class of small non-coding RNAs that regulate gene expression at the post-transcriptional level, have emerged as precise and versatile molecular tools. Since their discovery in Caenorhabditis elegans in 1993, miRNAs have been recognized as master regulators influencing plant development, signal transduction, stress tolerance and disease resistance. Their ability to fine-tune gene expression without permanently altering genomic sequences makes them valuable targets for molecular breeding strategies aimed at achieving high yield, resilience and sustainability in crops.

The increasing global population and climate variability have intensified the demand for sustainable food production, necessitating innovative approaches for crop improvement. Among modern biotechnological tools, proteomics has emerged as a powerful platform to study the complete set of proteins expressed under specific physiological conditions. Unlike genomics and transcriptomics, proteomics provides direct insight into functional molecules that regulate plant responses to abiotic stresses such as drought, salinity, heat, cold, waterlogging and toxic metal exposure. These stresses drastically reduce crop productivity by disrupting cellular homeostasis, metabolic pathways and signaling networks. Through advanced analytical techniques like mass spectrometry, two-dimensional gel electrophoresis, and isotopic labeling, proteomics enables the identification, quantification and characterization of stress-responsive proteins. Such information facilitates the discovery of biomarkers, validation of stress-associated genes and understanding of tolerance mechanisms. The integration of proteomic data into breeding and genetic engineering programs offers promising avenues for developing resilient, high-yielding crop varieties adaptable to changing environmental conditions.

The use of drone technology combined with deep learning is revolutionising the way plant diseases are detected and assessed in agriculture. Drones, equipped with high-resolution cameras and advanced imaging sensors, enable rapid and extensive monitoring of crop fields, capturing crucial visual and multispectral data from above. Deep learning models then analyse this information to accurately and swiftly identify early disease symptoms, even across vast and varied terrains. This synergy significantly reduces the need for manual field inspection, supports precise disease mapping, and enables timely, data-driven interventions. Integrating AI-powered systems with drones is also vital in regions with limited access to expert plant pathologists, democratizing disease management tools for all scales of farming. Ultimately, this integration enhances crop health monitoring efficiency, supports sustainable and productive farming practices and resilience in agricultural systems by empowering farmers with actionable intelligence for better disease management.

Transcriptomics, the study of the complete set of RNA transcripts produced by the genome, has become a central tool in the post-genomic era for understanding gene expression and regulation. It provides critical insights into cellular functions, developmental processes and responses to environmental or physiological stimuli. Techniques such as microarrays and RNA-sequencing (RNA-Seq) have revolutionized transcriptome profiling by enabling comprehensive quantification of both coding and non-coding RNAs. These methods not only identify novel transcripts and splicing variants but also uncover expression patterns underlying disease mechanisms, stress tolerance and developmental regulation. Transcriptome data further support integrative analyses in systems biology, linking gene activity to protein networks and metabolic pathways. Thus, transcriptome analysis serves as a foundational approach for discovering biomarkers, functional genes and therapeutic targets, significantly advancing personalized medicine and crop improvement.

Liquid organic manures (LOMs) are gaining prominence as sustainable alternatives to synthetic fertilizers in modern agriculture. Derived from organic wastes, plant residues and animal excreta, these nutrient-rich formulations provide essential macro- and micronutrients, beneficial microorganisms and bioactive compounds that enhance soil fertility and plant growth. Common types such as jeevamrutha, panchagavya, vermiwash and cow urine-based manures improve soil biological activity, nutrient cycling and disease resistance while maintaining ecological balance. The application of LOMs through soil drenching, foliar spray, seed treatment, and fertigation promotes root development, chlorophyll synthesis and yield improvement. Environmentally, they minimize pollution, recycle farm wastes and enhance sustainability at low cost. However, challenges such as low nutrient concentration, lack of standardization, and limited shelf life hinder their large-scale adoption. Integrating LOMs with other organic practices offers a viable pathway toward sustainable and eco-friendly agriculture.

The advanced technology by using Artificial intelligence in chicks hatching incubator allows Real-time monitoring the vital signs such as temperature, humidity, and oxygen levels remotely by using a smart phone. This technology can improve the survival rate of the chicks and thereby increase the hatchability percentage of the chicks by incorporating IoT and Arduino with a digital camera, it is possible to designed a smart incubator which are not only useful to farmers but also to those researchers who wants to study and monitor embryonic development of chicks accurately.

Cotton (Gossypium spp.) is one of the most economically important fiber crops worldwide, providing raw material for the textile industry. Upland cotton (G. hirsutum) accounts for about 90% of global production. Cotton fiber, a single elongated epidermal cell derived from the seed coat, undergoes a complex developmental process consisting of initiation, elongation, secondary cell wall thickening, and maturation. This process is regulated by intricate molecular networks involving various transcription factors (TFs) and metabolic pathways. Among these, MYB and HD-ZIP transcription factors play pivotal roles in epidermal cell differentiation, trichome and fiber initiation, and secondary wall biosynthesis. Carbohydrate and fatty acid metabolism contribute to fiber cell elongation and wall formation by supplying essential substrates and energy. Additionally, quantitative trait loci (QTLs) associated with fiber quality traits such as length and strength have been identified, offering valuable targets for genetic improvement. Understanding these regulatory mechanisms provides important insights for enhancing fiber yield and quality through molecular breeding and biotechnological approaches.

Cotton, tobacco and sugarcane are among the most economically important industrial crops that have undergone extensive domestication and evolutionary diversification across tropical and subtropical regions of the world. The genus Gossypium (cotton) includes about 50 species, of which four?G. hirsutum, G. barbadense, G. arboreum and G. herbaceum are cultivated. Cotton was independently domesticated in both the Old and New Worlds, with tetraploid species evolving through allopolyploidy between A- and D-genome progenitors. Tobacco (Nicotiana spp.), represented mainly by N. tabacum and N. rustica, originated in South America, with N. tabacum evolving through hybridization between N. sylvestris and N. tomentosiformis. The crop spread globally through early human trade and colonization. Sugarcane (Saccharum spp.), belonging to the Poaceae family, originated in the Indonesia? New Guinea region and was later hybridized with wild species to enhance yield and adaptability. Modern commercial cultivars are complex interspecific hybrids, primarily derived from crosses between S. officinarum and S. spontaneum. Collectively, these crops illustrate how natural evolution, polyploidy and human selection have shaped their domestication histories and global agricultural importance.

Plants encounter a wide range of abiotic and biotic stresses throughout their life cycle, necessitating precise regulation of gene expression for adaptation and survival. Gene expression is primarily controlled by specific genomic sequences known as cis-regulatory elements (CREs), which serve as binding sites for transcription factors and cis-regulatory modules (CRMs), which are clusters of CREs that include promoters, enhancers, silencers, and insulators. CRM?s are stretches of DNA, usually 100-1000 DNA base pairs in length, where a number of transcription factors can bind and regulate expression of nearby genes and regulate their transcription rates. Cis-regulatory elements (CREs) are short DNA motifs in gene promoters that act as molecular switches, controlling stress-responsive gene expression through transcription factor (TF) binding. In abiotic stress, pathways such as ABAdependent (AREB/ABF) and ABA-independent (DREB/CBF, NAC and MYB/MYC) regulate gene activation, as exemplified by the 116 to 2 bp promoter region of HRE2 in Arabidopsis bound by HAT22/ABIG1. Under biotic stress, CREs coordinate defence via., salicylic acid (SA), masonic acid (JA), and ethylene (ET) signaling, with NPR1 interacting with TGA TFs to activate pathogenesis-related genes and systemic acquired resistance.

Orphan genes, also known as taxonomically restricted genes (TRGs), represent speciesspecific coding sequences with no detectable homologues in other organisms. These genes are key drivers of evolutionary innovation and adaptation, often arising through mechanisms such as gene duplication, divergence, fusion, fission, horizontal gene transfer and retro position. While many orphan genes originate de novo from non-coding regions, others evolve beyond recognizable similarity to ancestral genes. The concept of orphan genes was first introduced during the yeast genome sequencing project in 1996, where they accounted for about 26% of the genome. Subsequent research established their widespread presence across taxa and highlighted their roles in lineage-specific traits and functional diversification. Identification of orphan genes primarily relies on computational approaches such as BLAST and phylostratigraphy, which detect sequence homology and estimate gene age, respectively. Despite challenges in detecting short or rapidly evolving sequences, orphan genes continue to provide valuable insights into genome evolution, species diversification, and the emergence of novel biological functions.