For understudied species like common vetch (Vicia sativa) and faba bean (Vicia faba), the combination of machine learning (ML) and meta-analysis (MA) has revolutionary promise for improving leguminous crops. Using MA and PRISMA-guided systematic review, this work synthesizes 115 peer-reviewed publications from 2015 to 2025 to assess machine learning applications in genomic and phenotypic trait prediction. The results show that ensemble approaches (e.g., Random Forest, XGBoost) perform better than standard models in disease resistance classification (AUC 0.88–0.91 via SVM) and yield prediction (R2 up to 0.92 in Phaseolus vulgaris). ML improves genomic selection (85–95% accuracy for flowering time GWAS) and root trait phenotyping (89% accuracy in faba bean drought adaptation) for Vicia species. Vicia villosa shows considerable phenotypic flexibility (CV 25–50%) but low model performance (F1-score 0.60–0.75 for winter survival), highlighting research gaps in tropical legumes, according to a meta-analysis. CNNs automate root architecture analysis (IoU 0.94); however, PLS regression is superior in NIRS-based nutritional trait prediction (R2 0.91 for protein). Data standards and the computing requirements for huge genomes (such the 13 Gb faba bean genome) are challenges. Precision breeding for nutritional quality and climatic resistance is made possible by the faster trait discovery made possible by the combination of ML and MA. In order to close the gap between model crops and ignored legumes, future efforts will focus on explainable AI, multi-omics integration, and cloud-based pipelines.

Cadmium (Cd) is a non-essential and toxic metal with high mobility in soils and strong bioaccumulative properties in fungi. This study presents novel data from an extensive biomonitoring campaign of wild mushrooms collected from 22 urban and rural locations across Leicestershire, UK. A total of 106 mushrooms, representing 14 species, were sampled and analysed by ICP-MS following acid digestion. Species identification was confirmed via DNA barcoding. Cd was detected in 78% of the samples, with concentrations ranging from below the detection limit (0.08 mg/kg) to 13.45 mg/kg dry weight. Contrary to expectations, Coprinus atramentarius (poisonous) showed the lowest Cd accumulation, while the edible species Agaricus bitorquis exhibited the highest levels (mean: 4.05 mg/kg). Geospatial analysis revealed that mushrooms from the North-West (2.75 mg/kg) and South-West (2.70 mg/kg) quadrants of Leicester contained significantly higher Cd levels compared to the South-East (0.64 mg/kg; p < 0.05). In A. bitorquis, caps accumulated significantly more Cd than stipes (4.05 vs. 2.20 mg/kg; p < 0.001), confirming species-specific tissue partitioning patterns. When benchmarked against the EU Maximum Allowable Concentration (MAC) for Cd in cultivated mushrooms (0.2 mg/kg), 47.2% of urban samples exceeded this regulatory threshold. Nevertheless, updated human health risk assessments (Hazard Quotient and Lifetime Cancer Risk) indicated no significant non-carcinogenic or carcinogenic risk from occasional consumption by either adults or children. These findings expand on previous moss-based biomonitoring studies in Leicester by directly demonstrating cadmium uptake in fungi growing in the same urban hotspots. The results reinforce the value of wild mushrooms as cost-effective sentinels for environmental metal(loid) pollution and underscore the need to integrate multiple bioindicator species in public health and soil management frameworks. Future research should explore seasonal variation, bioavailability, and co-exposure effects with other toxic elements.

Rice (Oryza sativa L.) remains vulnerable to bacterial blight, a major yield-limiting disease caused by Xanthomonas oryzae pv. oryzae (Xoo). This pathogen delivers transcription activator-like effectors (TALEs) that bind to effector-binding elements (EBEs) in the promoters of host susceptibility (S) OsSWEET genes. Deletions were made to disrupt these EBEs. A multiplexed prime-editing construct was designed to target four critical EBEs with deletions: 13 bp for PthXo1 (OsSWEET11), 10 bp for PthXo2 (OsSWEET13), and two EBEs in OsSWEET14 (11 bp for TalC and 24 bp for the shared site of PthXo3/AvrXa7/TalF). The deletions were based on previously validated resistance alleles and optimized to expand TALE resistance. Engineered pegRNAs (epegRNAs) were designed based on previously validated edits and incorporated with new deletions to broaden effector resistance. Each pegRNA included a primer-binding site (PBS), a reverse transcription (RT) template, and a paired nicking guide RNA (ngRNA) for PE3-mediated editing. The epegRNA–ngRNA cassettes were individually cloned into entry vectors using Golden Gate assembly. Colony PCR confirmed insert sizes of 986 bp for pegRNA scaffolds, and 347 bp (PL25025), 351 bp (PL25026), 355 bp (PL25027), and 348 bp (PL25028) for assembled modules. Sanger sequencing validated 100% identity and error-free constructs. All four verified cassettes were assembled into a single binary transformation vector (PL25034) using Multi-site Gateway recombination. LR product confirmation via colony PCR showed the expected 1,580 bp amplicons. Restriction digestion of PL25034 using Mlu I produced five distinct bands at 7,617 bp, 5,870 bp, 3,006 bp, 1,140 bp, and ~750 bp, consistent with the expected number of fragments generated. The final construct was transformed into Escherichia coli TOP10, yielding over 1,000 colonies per plate. Positive clones were archived in 15% glycerol stocks for long-term storage. This study provides foundational groundwork for modular, multiplexed prime editing of OsSWEET EBEs, advancing efforts to develop broad-spectrum, bacterial blight-resistant rice through precise promoter editing.

Nickel (Ni) is a moderately toxic metal emitted primarily by vehicular and industrial activities, with well-documented associations with allergic and respiratory effects. This study presents novel data on Ni bioaccumulation in wild mushrooms from urban parks in Leicester, the UK, and compares these findings with previous Ni data from native mosses used as passive biomonitors in the same locations. A total of 106 mushrooms from 14 species were collected and analysed for Ni by ICP-MS following acid digestion. Due to a limit of detection (LoD) of 3.40 mg/kg dry weight, 56.8% of values were censored. Among detectable samples, Ni concentrations ranged up to 90.50 mg/kg dw, with Coprinus atramentarius and Mycena citrinomarginata showing the highest mean values (56.36 and 17.40 mg/kg dw, respectively). By contrast, Agaricus bitorquis and Marasmius oreades showed no detectable accumulation (mean < 3.40 mg/kg). Spatially, elevated Ni levels were observed in mushrooms from parks located near high-traffic zones such as Abbey Park and Narborough Road. While previous moss-based studies revealed strong correlations between ambient Ni deposition and traffic exposure, mushroom Ni levels appeared more influenced by species-specific uptake capacity and root-zone exposure, rather than surface deposition alone. Notably, some areas with high Ni in mosses yielded mushrooms with low or non-detectable concentrations, indicating differences in exposure pathways and biological accumulation mechanisms. Although no regulatory thresholds exist for nickel in mushrooms, the concentrations detected in the wild edible species collected were relatively low. Human health risk assessments indicated no concern for occasional mushroom consumers. This study demonstrates the complementary value of active (fungi) and passive (moss) biomonitoring and highlights the importance of integrating multiple organism types when evaluating urban environmental pollution.

Turmeric is a profitable cash crop and the main source of income for the farmers of the tropical midhills in Nepal. A field experiment was carried out at the field genebank block of National Agriculture Genetic Resources Centre (NAGRC), NARC, with the aim of evaluating the agro-morphological traits and assessing the phenotypic diversity of turmeric landraces. This study involved the collection of 136 landraces from 62 districts across Nepal, characterized by an augmented block design. Thirteen quantitative and twelve qualitative traits were recorded and analyzed using R software, version 4.5. The Shannon–Weaver index revealed that qualitative traits exhibited a diversity range from low to high (0.19-0.99), while quantitative traits showed moderate to high diversity (0.38-0.91). The top three principal components accounted for 80.29% of the total phenotypic variation, with two components explaining 72.31%, primarily driven by plant height, leaf lamina length, rhizome breadth, petiole length, leaf lamina width, rhizome length, rhizome girth, rhizome weight and internode length. The cluster analysis divided the 136 accessions into two separate groups, with Cluster-1 being the largest, comprising 62.5% of the accessions, and Cluster-2 being the smaller group, representing 37.5% accessions. The phenotypic path analysis resolved that all of the traits showed a positive linear effect on the total yield except number of mother rhizomes, with an R squared value of 45.5%. The preliminary findings showed that NGRV0251, NGRV0179, and NGRV0219 were candidate landraces for the breeding program, as they were superior in terms of rhizome weight, number of mother rhizomes and total yield.

INTRODUCTION :

This self-developed initiative introduces SMABS (Smart Modular Agro-Bot Swarm)—a cutting-edge solution designed to revolutionize agricultural practices in developing regions. SMABS consists of compact, modular, solar-powered robotic units utilizing a hybrid energy system that combines biofuel with lithium-ion batteries for sustainable and uninterrupted operation. It addresses key agricultural challenges, including labor shortages, escalating production costs, and inefficient agro-input management.

METHODS:
SMABS functions through a decentralized, intelligent network where each robot communicates and collaborates using AI-based decision-making algorithms and real-time environmental sensors. Inspired by the swarm behavior of ants and bees, the system dynamically allocates tasks based on real-time field data. The project followed a multi-phase methodology comprising advanced digital prototyping, hardware–software integration, extensive field trials across diverse farm scales, and iterative refinement based on continuous farmer feedback. To ensure local adaptability, a Bengali and English voice-command interface and user-friendly DIY repair manuals were developed, enabling effective adoption by rural farming communities. The total estimated cost of SMABS (in USD) is approximately USD 4,000 (based on USD 1= BDT 120). This includes solar panels, robot hardware, and tools, sensors, AI-boards, batteries, software development, expert salaries, testing, promotion, and backup. With a dedicated team and consistent funding, the full system can be launched in under 8 months. One robot is needed for 0.05 ha-0.10 ha of land. Two to three robots are needed for 0.20 ha-1.0 ha of land. Three to five robots are needed for 1.0 ha-2.0 ha of land. Five to eight robots are needed for 2.0 ha-3.0 ha, while 10+ robots are needed for bigger lands.

RESULTS:

Field evaluations demonstrate that SMABS effectively performs a wide array of tasks including precision planting, targeted weeding, pest scouting, calibrated spraying, early disease detection with immediate responses, efficient harvesting, and continuous monitoring of soil and crop health. These trials revealed substantial gains in operational efficiency, reduced agrochemical usage, and improved resource management. The modular design ensures seamless scalability, making it suitable for both smallholder plots and large-scale farming operations.

CONCLUSION:
SMABS represents a paradigm shift in precision and decentralized agriculture, merging robotics, renewable energy, and AI to create a robust and scalable solution. It enhances farm productivity, promotes sustainability, and contributes meaningfully to global food security efforts. By empowering farmers with accessible, intelligent tools, SMABS sets a new benchmark for future-ready agriculture in resource-constrained regions.

This project aims to explore the biotechnological potential of native plant species from Mampuján, Bolívar (Colombia), a historically marginalized territory affected by armed conflict and displacement. Leveraging local biodiversity and ancestral knowledge, the initiative combines ethnobotanical surveys, STEM education, and 4.0 technologies to promote circular bioeconomy and social restoration. Approximately 100 promising plants will be identified through community knowledge and remote sensing via drone-assisted diagnosis. A subset will undergo taxonomic validation and extraction of essential oils or hydroethanolic extracts for chemical characterization and antimicrobial testing.

The antimicrobial activity of extracts will be evaluated against Pseudomonas aeruginosa, Staphylococcus aureus, Moniliophthora roreri, and Fusarium spp., using Kirby–Bauer and Minimum Inhibitory Concentration methods. Gas chromatography–mass spectrometry (GCMS) will determine chemical profiles and extraction efficiency. The results will inform the creation of a publicly accessible repository on promising species with potential pharmacological and agroindustrial applications. Moreover, the project prioritizes the empowerment of girls and adolescents (NNA) by integrating ancestral knowledge into scientific education, fostering social justice and environmental stewardship.

This work aligns with national science missions including “Science for Peace” and “Bioeconomy and Territory,” and contributes to Sustainable Development Goals (SDGs 1, 5, 8, 15). By facilitating knowledge exchange and capacity-building in circular economy practices, the project seeks to reduce economic disparity, strengthen local agroindustry, and provide tools for sustainable regional development. Ultimately, it positions bioprospecting as a path for socio-ecological resilience and health-oriented innovation within post-conflict communities.

Background: Cichorium intybus L. (chicory), a perennial herb of the Asteraceae family, is widely recognized for its medicinal value. However, isolating high-quality DNA from chicory remains challenging due to its rigid cell walls and the abundance of polyphenols and polysaccharides, which interfere with conventional extraction methods, often resulting in low yields and degraded DNA.

Objective: Our study aimed to optimize the cetyltrimethylammonium bromide (CTAB) method for efficient DNA extraction from chicory leaves, followed by validation using polymerase chain reaction (PCR).

Methods: Fresh, etiolated young chicory leaves were collected from four regions of Uttarakhand (India) and stored at -80°C before processing. Cell lysis was performed using pre-warmed 1x CTAB extraction buffer, omitting liquid nitrogen. RNase A treatment at 37°C removed RNA, followed by phase separation with a chloroform/isoamyl alcohol (24:1 v/v) mixture and DNA precipitation using isopropanol and 7.5M ammonium acetate. The extracted DNA was dissolved in 1x TE buffer and stored at -20°C. Yield and purity were assessed using a NanoDrop UV–Vis spectrophotometer, and integrity was confirmed via agarose gel electrophoresis. Primers for the 1-SST gene of chicory were designed using Primer3 and amplified by PCR.

Results: Method efficiency was improved by increasing the concentration of CTAB (2%) and polyvinylpyrrolidone (2%), along with the inclusion of 7.5 M ammonium acetate. The pre-warmed CTAB buffer eliminated the need for liquid nitrogen, simplifying the protocol. DNA yield averaged 64.56 ± 30.51 ng/μl, with an OD_{260/280 nm} of 1.75 ± 0.12. PCR successfully amplified a 2.01 kb fragment of the 1-SST gene in all samples, confirming the protocol’s reliability.

Conclusion: To the best of our knowledge, this is the first tailored protocol for DNA extraction from chicory. Compared to costly commercial kits, the optimized CTAB method is cost-effective, reproducible, and suitable for downstream molecular applications, thus advancing genomic research.

The integration of digital technologies into agriculture has become essential for optimizing resource use and improving production efficiency under controlled environments. In this context, greenhouse cultivation represents a strategic system for high-yield crop production, particularly under conditions of climatic uncertainty and land constraints. However, maintaining optimal microclimatic conditions remains a major operational challenge.

This study presents the development and experimental validation of an automated climate control system for greenhouse management, utilizing an Arduino Mega 2560 R3 microcontroller as the central processing unit. The system is equipped with multiple environmental sensors to continuously monitor key variables, including temperature, relative humidity, carbon dioxide concentration, and light intensity. A set of actuators responds to these inputs through a feedback control algorithm, enabling real-time regulation of the internal environment. The control system is interfaced with a desktop application for data acquisition, visualization, and user interaction.

The proposed platform demonstrates a cost-effective and scalable solution for smart greenhouse automation. It contributes to the broader framework of climate-smart and precision agriculture, supporting sustainable intensification and efficient resource management through digital innovation.

Introduction
This study attempts to understand the ecological changes and agrarian transformations brought about by the irrigation methods used in Uttar Pradesh from 1850 to 2000. Without considering ecological and agrarian concerns, new systems of irrigation based on the scientific revolution were introduced during the colonial period and further expanded during the post-colonial period.

Methodology:

The present study uses an integrated historical and environmental analytical approach to trace relations between irrigation means and their ecological effects. It depends on a variety of primary and secondary sources. It draws data from archival sources such as revenue settlement reports, irrigation records, newspapers, administrative records, and policy documents related to the colonial and post-colonial periods. It also includes critical analysis from secondary literature research articles and books.

Results :
The main argument of this study is that while irrigation brought initial economic gains by encouraging water-intensive crops and chemical use, it disrupted traditional water systems, altered cropping patterns, and contributed to environmental problems which have been profound and often irreversible. It also posed long-term challenges such as soil degradation, soil salinization, waterlogging and groundwater depletion, and agrarian distress. Traditional water systems were undermined, and disparities in agrarian society were widened.

Discussion:
This study concludes that irrigation must be understood not merely as a developmental intervention, but as an ecological force with enduring consequences. Recognizing the historical roots of environmental degradation is essential for formulating sustainable agrarian and irrigation policies today. This study suggests a need to rethink irrigation policy with consideration for environmental concerns.