In India’s economy, farming is crucial, making early detection of plant diseases an important task. This helps in reducing crop damage and preventing the diseases from spreading further. Numerous plants, such as corn, tomatoes, and potatoes, display evident symptoms of diseases on their leaves. These noticeable patterns can be employed to accurately predict the diseases and facilitate prompt intervention to reduce their impact. The customary method involves farmers or plant pathologists visually inspecting plant leaves and identifying the specific disease. This project involves a deep learning model designed for classifying plant diseases, utilizing CNNs for their proficiency in image classification. The model, which utilizes architectures like MobileNet, InceptionNet, ResNet, and ResNeXt, delivers faster and more accurate predictions than traditional manual methods. Notably, ResNeXt, with its added dimension of cardinality that aids in learning more complex features, achieved the highest accuracy, reaching 98.2%.

Diabetes mellitus is a prevalent chronic disease with significant implications for public health, including an expanded chance of coronary heart malady, stroke, persistent kidney illness, misery, and useful inability. In India, the predominance of diabetes among grownups matured 20 a long time and more seasoned rose from 5.5% in 1990 to 7.7% in 2016. Traditionally, diabetes management involves costly consultations and diagnostic tests, presenting challenges for timely diagnosis and treatment. Additionally, a comprehensive study was conducted to investigate the relationship between the incidence of type 2 diabetes mellitus (T2DM) and environmental exposure to arsenic in the form of air, water, and food pathways. The majority of the analyzed studies examined the levels of arsenic in water samples, with analyses of urine, blood, serum, and plasma samples coming next. Groundwater supplies may get contaminated by arsenic, especially in regions where arsenic deposits are naturally occurring or as a result of industrial activity. Additionally, various meals contain it, particularly rice, seafood, and poultry. Besides, it might be released into the environment by industrial processes such as coal combustion, smelting, and mining, which could lead to occupational exposure. There may be a genetic component to the association between arsenic exposure and the onset of diabetes. Ultimately, diabetes mellitus is enhanced by arsenic pollution through air, food, and drinking water. Advances in machine learning and telemedicine offer innovative solutions to address these challenges. Data mining, a crucial aspect of machine learning, facilitates the extraction of valuable insights from extensive datasets, enabling more efficient and effective diabetes management. This study explores a telemedicine-based system utilizing five classification techniques Tree, Naive Bayes, Support Vector Machine, and others to predict Type-2 diabetes. By leveraging real-time data analysis, the system aims to enhance early diagnosis and management of Type-2 diabetes, potentially preventing progression to critical conditions. The results evaluate the effectiveness of these models in a telemedicine context, identifying the bestperforming model to assist healthcare professionals in making informed decisions for early intervention and improved patient outcomes.

This research was initiated to study the groundwater potential zones using the Fuzzy logic technique at Lembangan Kadamaian, Kota Belud, Sabah, and its surroundings. The lithological units of this study mainly focus on the sedimentary rock of Wariu Formation, Crocker Formation, and Trusmadi Formation, including the quaternary alluvium deposition unit of Kota Belud. Based on the structural geology analysis results, the deformation trends are in the northwest-southeast direction. The interpretation of groundwater potential zones was made by using the ArcGIS Pro, R-studio Global Mapper, and several other mappingrelated software. Ten thematic maps that have been produced are lithological map, lineament density map, rainfall map, distant from river map, distant from lineament map, drainage density map, landform, and land cover map, Topographic Wetness Index (TWI) map, rock porosity map, curvature map, and slope steepness map. GIS techniques were used during the spatial analysis stage. All thematic maps have their class values and are based on field data, relevant department data, and remote sensing data. Further processes were done using R-studio, Fuzzy Toolset, and Raster Calculator. This process afterward will produce the groundwater potential map of the study area. The final result has been supported by the data of tube wells from the Department of Minerals and Geosciences, Sabah, and was validated using the ROC and AUC curve validation technique.

Heavy metal pollution released into the surface environment poses a significant threat, being hazardous to both the environment and living organisms. Phytoremediation thus appears as a viable technique to address heavy metal pollution in soils impacted by industrial effluents. To identify the growth performance of sunflower and cotton seeds under various concentrations of arsenic and chromium present in the tannery industrial wastewater in the Chengalpattu region, and to identify the accumulation of Arsenic(As)As and chromium (Cr) in the roots, shoots, and soil of these plants. This paper examined the usefulness of sunflower (Helianthus annuus) and cotton (Gossypium herbaceum) in eradicating Cr and As-polluted soils originating from tannery wastewater. In this experiment, Completely Randomized Block Design (CBRD) testing was performed, and the samples were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The accumulation of Cr in sunflowers was 120 mg.kg-1 in the roots and 25 mg.kg-1 in the above-ground parts. As accumulated to 85 mg.kg-1 in the roots and 15 mg.kg-1 in the above-ground parts. Similarly, cotton plants accumulated 90 mg.kg-1 of Cr in the roots and 20 mg.kg-1 in the above-ground parts. As accumulation in cotton plants was 100 mg.kg-1 in the roots and 30 mg.kg-1 in the aboveground parts. The study inferred that, in comparison to the other plants, the concentrations of Cr in sunflower roots were significantly higher, but cotton was found to have a better ability to take up As in the roots as well as in the aerial parts of the plant. It hence demonstrates the applicability of sunflower and cotton to support phytoremediation efforts sustainably within industrial environments to mitigate pollution and improve the quality of the soil.

Understanding aridity trends is crucial for climate adaptation strategies. This study analyzes the spatial and temporal fluctuations in aridity across Karnataka, India, over 63 years from 1958 to 2020 using the Aridity Index (AI). Monthly, seasonal, and annual AI values were calculated using precipitation and potential evapotranspiration data sourced from TerraClimate. The results indicate that approximately 74% (142,464 sq. km) of Karnataka is classified as dryland, ranging from semi-arid to dry subhumid zones, while 26% (49,416 sq. km) falls under more humid non-dryland areas. The Malnad and coastal regions are more humid compared to the predominantly semi-arid northern inland Karnataka. Temporal analysis between the periods 1958–1990 and 1991–2020 revealed that 6.24% of the land area shifted from semi-arid to dry subhumid, indicating increased moisture availability, whereas 0.43% shifted from dry subhumid to semi-arid, suggesting localized aridification. During the post-monsoon season, 14.12% of dryland areas transitioned to non-dryland, with substantial improvements in moisture availability observed in districts such as Uttara Kannada (59.21%) and Mandya (82.97%). Conversely, 1.5% of non-dryland areas converted to dryland, indicating localized decreases in water resources. Seasonal analysis revealed that 99.92% of the summer aridity status remained constant, while during the monsoon season, only 2.42% of dryland areas changed to non-dryland, reflecting stable monsoonal rainfall patterns. These findings highlight the significant influence of topography, monsoonal patterns, and water management on aridity dynamics in Karnataka. The study provides valuable insights for developing policies on climate adaptation, sustainable agriculture, and regional water resource management. Addressing the increasing trends in aridity is essential to reduce desertification risks and enhance the State’s resilience to climate change.

Manganese (Mn) contamination in groundwater is a global concern due to its harmful effects. The high concentration of Mn2+ in humans creates memory issues, decreased fertility, appetite loss, sleeplessness, sperm abnormalities, and ‘Manganism’. In this study, the isolation of thermophiles was followed by their assessment for MIC (minimum inhibitory concentration) and Mn bioremediation. We have isolated a total of 11 Mn-resistant bacterial strains of thermophiles with the identification of their bioremediation potential from the Tapt Kund, Soldhar, and Gauri Kund hot springs of Uttarakhand, India. Out of 11 strains, three isolates (TA8, SA9, and GA7) were identified with the highest metal resistance properties for toxic Mn2+. The metal tolerance capabilities of the strains were evaluated through MIC and the metal biosorption rate was estimated by the live cells bioremediation through thermophilic bacteria. ICP-MS (inductively coupled plasma mass spectrometry) was used to assess the Mn2+ removal rate of bacterial bioremediation. It turned out that every strain exhibited promising bioremediation potential and proved Mn-resistant. The bacterial strain TA8 exhibits the highest MIC (600 µg.L-1.) with a bioremediation rate of 98.34% for Mn2+. The bacterial strain SA9 has a MIC value of 525 µg.L-1, with a biosorption rate of 77.74% for Mn2+. The bacterial strain GA7 has a MIC of 475 µg.L-1, with an efficiency rate of 61.17% for Mn2+ removal. The most promising strain of thermophilic bacteria for Mn2+ bioremediation is the TA8, which has demonstrated the highest potential (98.34%) out of all the tested strains. The findings may have public health implications, as reducing manganese levels in groundwater can help mitigate health risks associated with Mn exposure. Also, this research enriches our knowledge of microbial bioremediation and its potential applications in environmental management. Ultimately, this research could offer a novel, economical, and environmentally beneficial approach to managing metal toxicity

Ericoid mycorrhiza (ErM) is an unexplored and understudied member of the mycorrhizal world, surprisingly belonging to Ascomycota and Basidiomycota instead of Glomeromycota (the phylum comprising fungi forming associations with higher plants). ErM obtained its etymology due to its symbiotic relationship with members of the Ericaceae Family. Just like any other mycorrhiza, ErM also helps its hosts through nitrogen uptake and phosphorus bioavailability and provides defense to host plants against various phytopathogens. It also takes part in the decomposition of organic matter and depolymerization of complex substances. These mycorrhizae are distributed across all continents except Antarctica. The majority of culturable ErM is spread across England, Australia, Canada, the United States etc. This review focuses on the literature survey on ErM, its taxonomy, and diversity alongside its functions. Our review also sheds light on the host range of the ericoid fungi, wherein, out of all the hosts, Salal (Gautheria shallon) has been identified as one of the most promising ones

Urbanization and industrialization have caused a ubiquity of microplastics in the environmental system. An effective elimination technique is required for microplastics from industrial effluent and other wastewater systems due to its growing threats to the ecosystem and human health. The present study endeavors to evaluate the potential of the membrane bioreactor (MBR) technique in the removal of microplastics from paper recycling industry wastewater effluent. The effectiveness of the MBR system was evaluated relative to the conventional method used in industry for wastewater treatment. The paper recycling industrial effluent consists of 148 pieces/L of microplastics. The conventional treatment plant’s effluent is used as an MBR system influent, and MBR removes 64.9% of the microplastic present after the conventional treatment plant, which is ascribed to the complementary actions of membrane filtration. MBR technology offers a reliable and workable plan to decrease the quantity of microplastics in industrial wastewater. It also offers a scalable solution that is consistent with sustainable environment management.

Air pollution is the atmospheric state in which the concentration of specific elements has adverse impacts on human health as well as the environment, including global warming, transportation disruptions, acid rain, and ozone layer depletion. Nowadays, a large portion of the world’s population lives in urban areas, where population growth and the increasing number of vehicles have significantly worsened air quality. Clean air is essential for the health and well-being of any region’s environment and its inhabitants. Henceforth, the primary focus of this research endeavor is to meticulously scrutinize the levels of key air pollutants, notably nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), leveraging satellite remote sensing data obtained from TROPOMI EXPLORER across a network of monitoring stations dispersed throughout Lucknow City. Additionally, it aims to meticulously dissect groundbased air quality monitoring data to validate and amalgamate the observations derived from satellite technology. Furthermore, it analyzes the distribution of concentrations of primary air pollutants, encompassing NO₂, SO₂, and PM₁₀, within Lucknow City, juxtaposing them against the stringent benchmarks stipulated by the World Health Organization (WHO) for air quality standards. Moreover, it endeavors to ascertain the deleterious health ramifications of air pollution by correlating air quality metrics with health outcomes among the denizens of Lucknow City through a meticulously crafted questionnaire survey. The scrutiny of satellite imagery unveiled a conspicuous escalation in the concentration of air pollution parameters vis-à-vis the WHO’s prescribed thresholds, portending consequential adverse ramifications for both the environment and human health.

Reptile and amphibian species in the Equatorial BTES face threats such as fragmentation, habitat loss, and climate change. Between 2019 and 2021, the richness and abundance of herpetofauna species was evaluated in the Lucarqui peasant community in Piura, northwest Peru. The objective of this research is to provide a preliminary list of species and understand their temporal frequency patterns. The study area was divided into specific zones: with anthropogenic activity, “crops” and “population centers”, where incidental catches and visual surveys were carried out, and without anthropogenic activity, “forests” and “ravines”, where transects of variable length and fixed width (2 m), the biological data obtained were analyzed with the iNEXT statistical tool, and a standardized methodology was provided for the calculation of the temporal frequency of recordings (FRT). The study identified 26 species: 7 amphibians and 19 reptiles. Amphibians dominated in abundance, while reptiles were rare. 85.71% (6) of amphibians and 47.36% (9) of registered reptiles are restricted to the Equatorial BTES. FRT patterns varied by habitat and time. These, along with wealth and abundance, were altered and reduced in areas influenced by human activity, crops, and population centers. It was found that there were still more species to be reported, especially reptiles. The study highlights the richness and vulnerability of the herpetofauna in the Equatorial BTES, reaffirming the urgent need for conservation strategies and continued research to ensure the protection and deep understanding of this valuable, fragile ecosystem.