University of Agricultural Sciences, Bangalore

Agricultural credit plays a vital role in supporting India’s rural economy and improving the livelihoods of small and marginal farmers. Although institutional credit to agriculture has increased substantially in recent years, equitable and effective access remains a persistent challenge. This paper adopts a systematic review approach based on 15 peer-reviewed studies published between 2021 and 2025 to examine the constraints affecting agricultural credit access in India. The review identifies five major categories of constraints: institutional/policy, economic, social/demographic, knowledge/behavioral, and structural/systemic. Key issues include gaps in policy implementation, stringent collateral requirements, bureaucratic delays, farmer indebtedness, disparities across social groups, limited financial literacy, and the continued dependence on informal lenders. The findings indicate that while initiatives such as Kisan Credit Cards, Joint Liability Groups, digital lending platforms, Farmer Producer Organizations, and crop insurance schemes offer partial solutions, a comprehensive and integrated approach is necessary. Strengthening last-mile delivery, simplifying procedures, enhancing awareness, and leveraging technology are critical for achieving inclusive and sustainable agricultural finance in India.

Bombyx mori, the domesticated silkworm, is one of the most economically significant and scientifically tractable insects. Since the publication of its draft genome sequence in 2004 and the subsequent production of a refined, consortium-level assembly in 2008, silkworm genomics has undergone a transformative evolution encompassing whole-genome resequencing, functional genomics, epigenomics, small RNA biology, and comparative and population genomics. These advances have not only illuminated the molecular mechanisms underlying silk production, development, immunity, and domestication, but have also catalysed a wide range of applications spanning biotechnology and biomedicine. This review synthesises current knowledge on silkworm genomics, tracing the progression from early sequencing initiatives to state-of-the-art genome editing strategies including the CRISPR/Cas9 system. A systematic literature search was conducted to identify peer-reviewed publications relevant to silkworm genomics and its applications. The primary databases consulted were Web of Science, Scopus, PubMed, and Google Scholar. The primary date range for inclusion was 2006 to 2026, with an exception for foundational studies published before 2006, which were included when they represented seminal contributions to the field. Special attention is given to the genomic architecture of silk protein genes, the regulatory roles of non-coding RNAs, epigenetic landscapes, and comparative genomics with related Lepidoptera. The biotechnological utility of the silkworm is explored in the context of silk-based biomaterials for tissue engineering, silk as a drug delivery scaffold, the production of chimeric spider silk fibres in transgenic silkworms, and the silkworm as an in vivo model for infectious disease research. The review further addresses the improvement of sericulture through molecular breeding, marker-assisted selection, and precision genome editing, as well as the genomic basis of innate immunity and disease resistance. Future directions in multi-omics integration, single-cell transcriptomics, synthetic biology, and pangenomics are discussed. Together, these developments affirm the central importance of silkworm genomics at the interface of fundamental biology and applied innovation.

Sericulture represents an important agro-based rural sector, particularly in North-East India where eri silk production is widely practiced. The present study aimed to evaluate the effect of digestive enzyme-enriched castor leaves on the quality of eri spun yarn produced by the eri silkworm (Samia ricini Donovan) during the spring (March–April) and autumn (September–October) seasons of 2024–2025. Completely Randomised Design (CRD) were used to set up the experiment. Three α-amylase and lipase concentrations (1%, 3%, and 5%) as well as a combination treatment (1% α-amylase + 1% lipase) were assessed and evaluated with an untreated control. Twist per inch (T.P.I), yarn size, breaking load, tenacity, elongation %, and other aspects of yarn quality were examined. Among treatments, α-amylase at 3% concentration consistently recorded superior performance. In spring, it produced the highest yarn size (19.22 S), breaking load (0.72 kg), tenacity (0.81 g/d), elongation (18.38%), and TPI (18). Similar trends were observed in autumn, though overall values were comparatively lower than spring. The combination treatment also showed improvement over control, particularly in tenacity and elongation. Across all parameters, the control group recorded the lowest values, confirming the beneficial role of digestive enzymes in enhancing silk fibre characteristics. Seasonal comparison indicated that spring conditions were more favourable than autumn, resulting in better yarn quality traits. The study concludes that fortification of castor leaves with digestive enzymes, especially α-amylase at 3% concentration, significantly enhances eri spun yarn quality. This nutritional supplementation strategy offers a practical and effective approach for improving silk productivity and yarn performance under varying seasonal conditions.

The integration of machine learning (ML) techniques with biomedical implant systems has markedly transformed patient care, diagnostics, and therapy. MATLAB, esteemed for its robust computational capabilities and extensive toolboxes, has emerged as a preferred platform for the development and implementation of ML algorithms in biomedical applications. This review examines the current state of ML implementation using MATLAB across various biomedical implant applications, including neural prosthetics, cardiac implants, orthopedic devices, and continuous monitoring systems. We discuss key ML algorithms, such as support vector machines, neural networks, random forests, and deep learning architectures, which have been successfully implemented in MATLAB for signal processing, pattern recognition, and predictive analytics in implantable devices. This review also addresses challenges such as data quality, computational constraints, regulatory compliance, and real-time processing requirements. Future directions emphasize the need for edge computing integration, federated learning approaches, and enhanced interpretability of ML models in clinical settings. This comprehensive analysis provides researchers and practitioners with insights into leveraging MATLAB to advance intelligent biomedical implant technologies.

The study explores the use of rainfall patterns and maize types in Chamarajanagara district, Karnataka, India, where rainfall varies significantly from place to place and year to year. The researchers used data from weather stations collected every month from January to December 2021 and tested 6 methods to create full maps: Inverse distance weighted (IDW), Spline, Natural neighbour, Trend, Kriging and Topo to Raster. The results show that weather plays a significant role in maize yields, with timing and amount playing a crucial role. Sowing windows or the best weeks to plant seeds, are crucial in this region, as planting too early or too late can lead to water stress. The hybrid MAH 14-5 genotype was found to be particularly effective when sown in the first week of July, resulting in stronger growth, higher nutrition levels and reduced anti-nutritional factors. These findings emphasise the importance of selecting the right planting time to maximise water use efficiency, harness soil conditions and utilise sunlight intensity. Farmers who match sowing to rain peaks can boost yields without extra inputs. The study also highlighted how rain changes over time and space in the district, with some spots experiencing steady showers in June and others experiencing heavy bursts in August, leading to uneven crop growth. Smart planning involves tailoring sowing windows to local rain forecasts, choosing genotypes that fit the area's weather and aligning tasks like fertilising or weeding with rainfall. These steps help farmers adapt and achieve reliable harvests year after year.

Grape (Vitis vinifera L.) constitutes a fruit crop of considerable economic significance; however, its productivity is markedly limited by several fungal pathogens, notably downy mildew (Plasmopara viticola), powdery mildew (Erysiphe necator), anthracnose (Colletotrichum gloeosporioides), and Botrytis bunch rot (Botrytis cinerea). Among these diseases, anthracnose incited by C. gloeosporioides is particularly destructive, frequently resulting in substantial yield losses, deterioration of fruit quality, and reduced market value when environmental conditions favour disease development. The intensity of anthracnose was evaluated in eight grape cultivars during the back-pruning seasons of 2024 and 2025 at Raichur. Among the cultivars tested, Thompson Seedless demonstrated the earliest manifestation of disease symptoms and the most rapid disease advancement, reaching a per cent disease index (PDI) of 100 in both seasons. This cultivar also recorded the highest values for the area under the disease progress curve (AUDPC) and the apparent infection rate. Fantacy Seedless and Medica showed comparatively slower disease progression, while Sharad Seedless, K. R. White and Manjari Naveen exhibited moderate disease development. however, all varieties were classified as highly susceptible. Weekly monitoring of anthracnose in Thompson Seedless during Kharif 2024 and 2025 showed disease appearance in the 27thSMW (8.50 PDI) in 2024 and in the 24thSMW (7.13 PDI) in 2025, reaching 100 PDI by the 39thand 35thSMW, respectively. The highest apparent infection rate was recorded at the 36thSMW (r = 0.105) in 2024 and at the 29thSMW (r = 0.119) in 2025, with cumulative AUDPC values of 4319.00 and 4110.30, respectively, highlighting rapid disease buildup and the need for timely disease management during back pruning.

A study was conducted during 2024-25 at the Department of Sericulture, UAS, GKVK, Bengaluru, to evaluate the seasonal incidence of thrips and to identify resistant mulberry accessions from one hundred mulberry accession. Thrips counts were taken at 15, 30, 45 and 60 days after pruning during rabi, summer and kharif seasons, and accessions were classified into resistance groups based on mean thrips population. From these groups, eleven accessions viz., ME-86, ME-01, ME-52, M-66, ME-18, ME-84, MI-79, MI-524, MI-517, MI-32 and MR-2 were selected for biochemical analysis under healthy and thrips-infested conditions. Thrips infestation significantly reduced total chlorophyll (7-13%), chlorophyll-a (7-15%), and chlorophyll-b (4-12%) across all accessions. Nutrients such as nitrogen (6-13%), potassium (7-13%), protein (6-13%), moisture (8-16%) and carbohydrates (6-12%) also declined, with the highest losses observed in highly susceptible accessions viz., MI-79, MI-517, MI-524, MI-32 and MR-2. Moderately resistant accessions like ME-86, ME-01, ME-52 and M-66 showed only small reductions in nitrogen (6-8%), potassium (6-7%) and protein (6-8%), indicating stronger physiological tolerance to thrips feeding. In contrast, secondary metabolites increased in resistant accessions viz., ME-86, ME-01 and ME-52 showed higher phenol (3.66-3.85 mg/g) and tannin levels (0.96-1.01 mg/g), while susceptible accessions such as MR-2, MI-79 and MI-524 showed slight decreases. Accessions with higher nutrient content viz., ME-84, ME-18 and the highly susceptible group viz., MI-79, MI-24, MI-517, MI-32 and MR-2 supported more thrips, whereas those with higher defensive compounds restricted pest buildup effectively. These findings help identify stable, thrips-tolerant accessions viz., ME-86, ME-01, ME-52 and M-66 for improving mulberry cultivation and sustainable sericulture.

The escalating demand for global food security is increasingly threatened by insect pests, which are responsible for an estimated 20-40% of annual crop losses worldwide. Excessive dependence on conventional chemical pesticides has generated severe collateral consequences, including the evolution of resistance in more than 600 arthropod species, environmental contamination, and substantial risks to human health and biodiversity. Nanotechnology defined as the manipulation of materials at the nanoscale (1–100 nm) has emerged as a transformative approach to mitigate these challenges. Owing to their distinctive physicochemical attributes, such as high surface-area-to-volume ratio, enhanced reactivity, and tunable surface functionality, nanomaterials enable the development of advanced pest-management tools, including nano-pesticides, smart delivery platforms (nano-encapsulates, nanoemulsions, and nanogels), and precision biosensors. These innovations enhance solubility, stability, and targeted delivery of active ingredients, allowing controlled release kinetics, reduced application rates, and minimized environmental burden. This review critically examines the contemporary landscape of nanotechnology in insect pest management, tracing the historical evolution of pesticide use, outlining fundamental principles of nanomaterial synthesis and characterization, and presenting a systematic classification of nano-formulations with their underlying mechanisms of action. It further consolidates recent advances in field and post-harvest applications, evaluates the integration of nanosystems in pest surveillance and precision agriculture, and provides a balanced appraisal of environmental and human toxicological implications. Additionally, the review analyzes emerging regulatory frameworks, commercialization barriers, and future research directions, emphasizing that responsible and evidence-based innovation in nano-agriculture is essential for the development of sustainable, efficient, and resilient Integrated Pest Management (IPM) strategies.

Seed health testing is a cornerstone of global food security, yet traditional diagnostic workflows often struggle to balance speed, sensitivity, and regulatory confidence under low-prevalence and heterogeneous seed lot conditions. This review synthesizes recent advances in molecular diagnostics (PCR, qPCR, LAMP, and digital PCR), non-destructive imaging technologies (hyperspectral, X-ray, and thermal imaging), and data-driven analytical approaches for pathogen detection in seeds. Emphasis is placed on the practical integration of these tools within high-throughput, ISO/IEC 17025-compliant laboratory workflows, highlighting their respective strengths, limitations, and roles in risk-based decision-making. Comparative discussions address cost, sensitivity, turnaround time, and field deployability across diagnostic platforms, supported by crop- and pathogen-specific examples. Emerging approaches such as CRISPR-based biosensing, advanced sequencing, and imaging-assisted analytics are discussed in the context of validation, regulatory acceptance, and operational feasibility. By focusing on implementation rather than conceptual frameworks, this review provides a pragmatic reference for laboratories, regulators, and seed companies seeking to modernize seed health testing while safeguarding trade integrity and biosecurity.