The changes in vegetation cover in Mecca City, Saudi Arabia, from 2014 to 2024 are analyzed in this research, utilizing the Normalized Difference Vegetation Index (NDVI) from Landsat imagery. The NDVI provides valuable information on vegetation density and health, offering insights into environmental changes over time. The findings suggest that the city witnessed a positive shift in vegetation health over the span of ten years, which was mainly attributed to an increase in precipitation (66.9 mm) and human-driven water conservation initiatives. The vegetation in the research area was classified into four groups: Healthy Vegetation, Moderately Healthy Vegetation, Unhealthy or No Vegetation, and No Vegetation. The research revealed a 2.07% uptick in Healthy Vegetation between 2014 and 2024, accompanied by a decrease in bare land. Alongside looking back at past data, this study employs machine learning models to estimate NDVI values for 2030, using historical data from 2015 to 2023. The models utilized encompass Artificial Neural Networks (ANNs), Decision Tree Regression, and Random Forest Regression, among other models. The ANN model anticipates an upward NDVI trend, projecting a 2030 NDVI of 0.0313, indicating potential enhancements in vegetation health if current conditions persist. Conversely, the Random Forest model anticipates a reduction in vegetation coverage, projecting an NDVI of 0.01462 for the same year, suggesting potential degradation under specific circumstances. The Decision Tree model aligns more closely with the ANN, projecting an NDVI of 0.01654. These varied projections underscore both the potential for vegetation recovery and the possibility of decline, contingent on environmental management practices and climate variability. The results underscore the significance of adaptable land and resource management strategies, particularly in dry regions like Mecca City, to guarantee sustainable vegetation growth and biodiversity conservation amid ongoing climate changes.

Lead (Pb) is a persistent neurotoxic metal(loid) with widespread occurrence in urban environments. This study evaluated Pb concentrations in wild mushrooms collected from 22 sites across Leicestershire, UK, with a focus on species-specific accumulation and intra-species tissue partitioning. A total of 106 mushrooms, representing 14 species, were analysed using ICP-MS following acid digestion. Species identification was confirmed through DNA barcoding. Pb was detected in 91% of the samples, with concentrations ranging from 0.30 to 10.57 mg/kg dry weight. The highest levels were observed in Coprinus atramentarius (mean: 6.64 mg/kg), followed by Mycena citrinomarginata (4.64), Panaeolus foenisecii (3.94), Agaricus bitorquis (2.37), and Marasmius oreades (1.32) (p < 0.05). Compared to earlier single-species studies focusing on A. bitorquis, this work provides a broader comparative analysis across multiple taxa. In M. citrinomarginata, Pb was significantly more concentrated in caps than stipes (4.23 vs. 2.51 mg/kg; p < 0.01), supporting known patterns of apical bioaccumulation. Pb concentrations exceeded the EU Maximum Allowable Concentration for cultivated mushrooms (0.3 mg/kg) in 38.5% of the dataset. Geospatial analysis identified elevated Pb levels in samples from the North-West and North-East quadrants of Leicester, consistent with legacy land-use patterns, although differences were not statistically significant. Updated health risk assessments confirmed no significant non-carcinogenic or carcinogenic risk for adults or children from occasional consumption. This study expands previous Pb biomonitoring in mushrooms by identifying additional high-accumulating species and reinforces the value of fungal biomonitors in environmental surveillance. These findings support the integration of wild mushroom data with soil and atmospheric Pb mapping in urban health policy and land management.

Climate change has emerged as a critical threat to agriculture in Sylhet, Bangladesh, a region distinguished by its diverse agro-ecological zones, including haor wetlands, hilly tea plantations, and fertile floodplains. Increasingly erratic weather patterns such as unseasonal rainfall, flash floods, and prolonged droughts are destabilizing agricultural productivity, endangering rural livelihoods, and intensifying food insecurity. Although Bangladesh has developed comprehensive climate strategies—such as the Bangladesh Climate Change Strategy and Action Plan (BCCSAP), the National Adaptation Plan (NAP), and the Delta Plan 2100—their effective implementation at the local level remains questionable. This study investigates the policy gaps and evaluates the role of local governance in translating national climate agendas into actionable interventions for climate-resilient agriculture in Sylhet. This study adopted a mixed-methods approach. Quantitative data were collected from a survey of 150 farmers across three agro-ecological zones in Sylhet, assessing their perceptions, losses, and access to institutional support related to climate impacts. Additionally, 20 key informant interviews were conducted with officials from Union Parishads, Upazila administrations, agricultural extension departments, and local NGOs. A policy content analysis was also carried out to examine how national frameworks are operationalized within local administrative and planning structures. Findings reveal significant policy and implementation gaps. These include inadequate decentralization of climate adaptation funds, weak coordination between government departments, limited training of extension workers on climate-resilient farming, and the absence of localized climate risk assessments in development planning. Many farmers reported receiving little to no institutional support during climate shocks, relying instead on traditional knowledge and informal networks. This study recommends greater devolution of climate-related decision-making and financing to the Union and Upazila, targeted training for local officials, improved integration of local data into national planning, and farmer-inclusive adaptation strategies. Strengthening local governance is essential to building long-term resilience in Sylhet’s climate-vulnerable agricultural sector.

Rice yields can be improved with the optimum plant population and nitrogen levels. A field experiment was conducted at Krishi Vigyan Kendra, Panipat (Haryana), during kharif 2023 to explore the effect of the plant population dynamics and nitrogen management on Basmati rice. In this study, three different plant populations, viz. P1, P2, and P3 (33, 25, and 20 plants m-2), and five nitrogen levels, viz. N1, N2, N3, N4, and N5 (100% RDN; 100 RDN + two sprays of nano nitrogen at 40-50 and 60-70 DAT; LCC-based nitrogen scheduling; 125% RDN; and 150% RDN), kept as the main plot and a sub plot, respectively, were replicated thrice in a split-plot design. The results revealed that P2N5 had the highest yield (5292 kg ha-1), which was higher than that (3.14~11.05) under the other treatments. This can be attributed to a higher number of grains in panicle-1. However, the combination of P2N5 leads to better gross returns and a better benefit:cost ratio. This study suggests that P2N5 treatments could enhance rice yields up to a significant level.

Coffee husk represents the largest residue from Brazilian coffee cultivation. However, it is rich in bioactive compounds such as chlorogenic acids, trigonelline, and phenolic compounds, which possess significant antioxidant, antimicrobial, and probiotic effects. These characteristics make the husk a natural alternative to synthetic extracts, valued by the food industry. This study aimed to analyze the impact of adding rough lemon (Citrus x limonia) and Sicilian lemon (Citrus limon), at different fermentation times, on the antioxidant, antimicrobial, and probiotic characteristics of coffee husk, aiming for sustainable use of this residue and promotion of additional income for coffee farmers. The coffee husk fermentation methods conducted were as follows: with rough lemon (A), coffee husk only (B), and with Sicilian lemon (C), over periods of 12 (1), 36 (2), and 60 hours (3), totaling nine methods (A1, A2, A3, B1, B2, B3, C1, C2, and C3). After fermentation, the husks were dried to 12.35% moisture content (dry basis) and analyzed for pH, titratable acidity, phenolic compounds, trigonelline, and chlorogenic acids. Additionally, antimicrobial capacity tests of the husks were performed using Staphylococcus aureus and Salmonella enteriditis as pathogenic microorganisms and Lactobacillus acidophilus as a probiotic. The results showed that the addition of lemon during husk fermentation acts as a pH reducer and increases the titratable acidity of the extracts, especially within the first 36 hours of fermentation. In general, phenolic compounds decreased during fermentation, except in treatment B3, which showed a significant increase in phenolic compounds and titratable acidity, indicating an improvement in the husk's antioxidant activity over time. Treatment A3 increased trigonelline content, suggesting higher antioxidant activity, although it reduced phenolic compounds and chlorogenic acids. Treatment C1 showed the best overall results, suggesting the positive effect of Sicilian lemon after 12 hours of fermentation. Regarding microbiological analyses, the addition of lemon provided an antimicrobial effect against the pathogens, but also against the probiotic bacteria. This effect was also observed in treatment B2, suggesting that 36 hours of husk fermentation benefited the antimicrobial effect. None of the treatments benefited the development of L. acidophilus. The use of lemon in fermentation proved to be a promising strategy for adding value to coffee residues, offering natural alternatives for industries. However, further studies are necessary to optimize the fermentative process and maximize the utilization of bioactive compounds.

Introduction: Wheat is a major crop in Pakistan since it guarantees the country's food supply and economic stability. Effective yield prediction is necessary to maximize output, reduce waste following crop harvesting, and save resources. Conventional approaches to yield prediction are frequently imprecise and fail to recognize how climatic conditions impact crop growth. This research aims to develop an AI-driven framework for wheat yield prediction.

Methods: This research uses 23 years of historical agro-meteorological data, with features including evapotranspiration (mm), mean sea level pressure (hPa), mean soil moisture (m³/m³, 7-28 cm depth), mean soil moisture available to plants (fraction, 7-28 cm depth), mean relative humidity (%), minimum temperature (°C, 2m elevation), and mean soil temperature (°C, 7-28 cm depth), retrieved within the archives of Meteoblue and actual historical yield (acres) from the Pakistan Bureau of Statistic. Various machine learning models were trained and tested, and after preprocessing and converting to a time series with lagged features, a two-layer Long Short-Term Memory (LSTM) network performed the best in all evaluation measures.

Results: Early tests showed good results with the proposed models, but the deep learning-based LSTM model was used because of its strong ability with time-series data, improving the accuracy of forecasting yields. Using this method, the features are captured for their time dependencies, leading to accurate yield predictions with an R² score of 0.979, a mean squared error (MSE) of 0.0004, a root mean squared error (RMSE) of 0.0201, and a mean absolute error (MAE) of 0.0111 on the test set.

Conclusion: In conclusion, the results demonstrate that in environmentally sensitive regions (like Pakistan), deep learning is a suitable approach for agriculture forecasting. Future research should focus on improving the generalizability of the model and applying the technique to other staple crops for more agricultural relevance.

Solanum muricatum Aiton, commonly known as sweet cucumber, is native to South America and had historical significance in pre-Incan civilizations such as the Moche, Nazca, and Wari. This study presents the morphological and preliminary phytochemical characterization of S. muricatum germplasm conserved at the Universidad Nacional de San Cristóbal de Huamanga (UNSCH), Peru. Two germplasm collection events were conducted. The first, in Ayacucho (Gutiérrez, 2023), yielded 24 accessions evaluated using a simple 5×5 lattice design with three replications, including Capsicum pubescens as an outgroup. Fifty qualitative and twenty-seven quantitative traits were assessed using IPGRI and COMAV descriptors. Cluster analysis identified four distinct groups, validating differentiation, with C. pubescens forming a separate clade. Grouping was also observed based on vegetative phenology, inflorescence, fruit, and seed traits. An ANOVA revealed highly significant differences among the accessions (p < 0.01) in agronomic traits such as the fruit count per inflorescence, fruit count per plant, and yield per plant. The second collection (Ramirez, 2024) included accessions from Cusco, Apurímac, Ayacucho, Ica, Lima, Cajamarca, and Lambayeque, contributing 35 accessions. Cluster analysis revealed seven distinct groups, and an ANOVA again showed significant variation (p < 0.01). The current ex situ germplasm bank now contains 59 asexually propagated accessions and 77 segregating lines grown from botanical seeds—126 genotypes in total. Phytochemical profiling of fruit peel from 12 morphologically representative accessions, alongside Physalis peruviana and Solanum betaceum controls, was conducted via HPLC using standards of caffeic acid, rosmarinic acid, cinnamic acid, quercetin, rutin, resveratrol, and chlorogenic acid. The results showed substantial variability in the total phenols (0.55–1.71 mg GAE/100 mg), antioxidant capacity (0.42–0.78 mg vitamin C/100 mg), flavonoids (0.13–0.24 mg quercetin/100 mg), hydroxycinnamic acids (0.25–1.11 mg caffeic acid/100 mg), carotenoids (6.62–33.02 mg/100 mg), and DPPH inhibition (IC₅₀: 103.23–1402.95 μg). Carotenoids and chlorogenic acid were the predominant compounds. These findings reveal significant genetic and phytochemical diversity within S. muricatum, supporting its potential for further biotechnological enhancement through genomics, transcriptomics, and metabolomics research at the UNSCH.

The rapid pace of global population growth and urbanization continues to deplete arable land, emphasizing the urgent need for sustainable food production systems. This study examines the effectiveness of hydroponic vertical farming systems (HVFSs) equipped with light-emitting diode (LED) technology in cultivating Chinese kale (Brassica oleracea var. alboglabra). Specifically, it evaluates the impact of three distinct LED treatments—white LEDs (WL), 20% red + 80% blue (20%RL:80%BL), and 80% red + 20% blue (80%RL:20%BL)—on morphological development and phytochemical accumulation.

Plants were grown under controlled conditions (pH 5.8–6.5, EC 2.0–3.0 dSm⁻¹) and assessed at weeks 2, 4, and 6 post-transplanting. The 80%RL:20%BL treatment yielded superior fresh weight, leaf area, root length, and dry biomass, indicating that higher red light proportions significantly enhance morphological growth (P<0.05). In contrast, the 20%RL:80%BL treatment resulted in the highest chlorophyll (53.60 mg/g), anthocyanin (11.30 units), and total phenolic content (1.46 mg GAE/g), suggesting blue-light dominance improves phytochemical profiles.

Interestingly, leaf number and maximum quantum yield (QY) remained statistically similar across treatments, though all QY values fell below the ideal benchmark of ~0.83, indicating light-induced stress across the board. Notably, Chinese kale grown under WL exhibited the greatest stem elongation, likely triggered by a low red to far-red ratio that induced shade avoidance responses.

Overall, an 80%-RL:20%-BL ratio emerges as the optimal treatment for promoting vegetative growth, whereas a 20%-RL:80%-BL ratio proves more effective for enhancing phytochemical enrichment. These insights offer practical guidance for optimizing LED configurations in HVFS, balancing yield and nutritional quality for future urban agricultural systems.

Postharvest losses of fruits and vegetables remain a major challenge in tropical regions such as Bangladesh, primarily due to a lack of affordable and effective storage solutions. This study presents the design and development of a low-cost, solar-powered smart evaporative cooling storage system aimed at short-term storage of perishable produce while preserving its physicochemical qualities. The system comprises four integrated units: a cooling unit, a controller unit, a storage chamber, and a solar-powered energy supply. The key components include axial cooling fans, a thickened cellulose cooling pad, a submersible DC water pump, and dual exhaust fans. The solar power setup consists of two 150 W photovoltaic panels, a 12/24 V–30 A charge controller, and a 500 W battery system (dual 12V batteries connected in parallel). The insulated storage cabinet has a volume of 2.04 m³ and utilizes a hybrid evaporative cooling mechanism. Sensors were deployed to monitor the temperature, relative humidity, CO₂, and ethylene (C₂H₄) concentrations, with real-time data transmission to an IoT-based cloud platform for performance analysis. Under no-load conditions, the chamber maintained a temperature range of 26.02–29.45°C and relative humidity of 86.61–93.39%, achieving a cooling efficiency of between 49.71% and 83.86%. Tomato fruits at varying maturity stages were stored and compared against those kept under ambient and conventional refrigeration conditions. The results showed that the developed system effectively reduced physiological and biochemical deterioration, maintaining their postharvest quality and extending their shelf life. This study highlights the potential of developing a sustainable, energy-efficient, and affordable storage technology for smallholder farmers in tropical regions, contributing to reduced postharvest losses and improved food security.

Alternaria blight, caused by the necrotrophic fungi Alternaria brassicicola and A. brassicae, is one of the most devastating and widespread diseases of oilseed Brassicas worldwide. Conventional breeding efforts to develop resistant cultivars have been unsuccessful due to the lack of suitable resistance sources among cultivated species. Sinapis alba, a crop wild relative (CWR) of oilseed Brassica spp., has been reported to exhibit considerable resistance to Alternaria blight. In the present study, we attempted to clone and characterize two important pathogenesis-related (PR) genes from S. alba, endochitinase and glucan endo-1,3-beta-glucosidase, and designed constructs for their functional characterization through overexpression. The genes were selected from a transcriptomic dataset of differentially expressed genes (DEGs), generated in a previous study, based on their expression patterns in S. alba and B. rapa following inoculation with A. brassicicola. The differential expression patterns of the PR genes were validated through qPCR. Furthermore, gene ontology analysis and protein–protein co-expression network studies provided insights into the functional roles of these genes in defense against the necrotroph. The complete coding sequences (CDSs) of the genes were then isolated from S. alba via PCR amplification, cloned into the pGEM-T Easy cloning vector, and subsequently into the pCAMBIA1301 binary vector. The constructs were validated by transient expression assays through agroinfiltration in Nicotiana benthamiana and B. rapa, followed by qPCR analysis. The constructs prepared for the overexpression of endochitinase and glucan endo-1,3-beta-glucosidase will further be used for stable transformation in B. rapa for functional validation through appropriate bioassays.