Clove or cengkeh (Syzygium aromaticum) is one of Indonesia’s commodities with high domestic and international potential, considering that this plant is used as raw material for the cigarette industry. Therefore, it is necessary to optimize the production of Indonesian cloves, one of which is by using growth stimulators such as plant growth regulators (PGR). This study uses gibberellic acid (GA3) and smoke water as exogenous growth triggers. The treatment given was soaking S. aromaticum seeds in gibberellic acid (GA3) and liquid smoke for 24 h. The GA3 concentrations used were 100 ppm, 75 ppm, 50 ppm, and 25 ppm. Smoke water was obtained from the pyrolysis of coconut shells, and the concentrations used were 0.5%, 1%, 2%, and 3%. Observations were conducted for 11 weeks and divided into two phases, namely the germination phase and the vegetative growth phase. Parameters measured included germination percentage, radicle, and plumula length in the first phase, root length, plant height, and number and area of leaves in the second phase. The best results were achieved with the soaking treatment using 0.5% smoke water, which showed a significant increase in all observed growth parameters. This is due to the content of karrikin in smoke water, which acts like a growth hormone and triggers the performance of other growth hormones. In addition, karrikin plays an active role in the germination process by changing the morphology of the seeds.

In the realm of precision agriculture, a pivotal challenge lies in the detection, identification, and grading of crop diseases. This multifaceted task necessitates the involvement of expert human resources and time-sensitive actions aimed at mitigating the risks of production losses and the rapid spread of diseases. The effectiveness of the majority of developed systems in this domain hinges on the quality of image features and disease segmentation accuracy. This paper presents a comprehensive research endeavor in the domain of Content-Based Image Retrieval (CBIR), specifically tailored to detect and classify leaf diseases. The proposed system integrates both color and texture features to underpin its functionality, providing a robust framework for accurate disease detection. By leveraging advanced image processing techniques, the system enhances the precision of disease identification, which is crucial for timely and effective intervention in agricultural practices. To evaluate the system’s performance, maize leaves afflicted by rust and blight serve as prime candidates for testing. These diseases were chosen due to their prevalence and significant impact on crop yield. The experimental results demonstrate that the developed system consistently excels in its disease detection and identification tasks, boasting an impressive efficiency rate of 98.33%. This high level of accuracy underscores the potential of the system to be a valuable tool in precision agriculture, aiding farmers and agricultural experts in maintaining healthy crops and optimizing production. The integration of color and texture features not only improves the detection accuracy but also provides a comprehensive understanding of the disease characteristics. This dual-feature approach ensures that the system can distinguish between different types of diseases with high precision, making it a versatile solution for various agricultural applications. The findings of this research highlight the importance of advanced image analysis techniques in enhancing the capabilities of disease detection systems, paving the way for more efficient and effective agricultural practices.

Watershed Development is a very common phenomenon in the river basins in India due to its dynamic and continuously changing nature, which are interconnected via. Land use/land cover (LULC) change and water poverty scenario over time. In the present study, the samples were chosen from seven sampled villages for the Water Poverty Index (WPI) in the upper Tons River Basin. Among them, Ghunwara and Maihar Village exhibit the highest and lowest WPI, i.e., 98.1 and 62.91 out of 100, respectively. This indicates that villages with a high WPI face challenges in their water requirements, regardless of the seasonal river serving the basin area. Conversely, villages with a low WPI can satisfy their water needs solely from the basin. The present analysis of the Upper Tons River Basin suggests that Land Use and Land Cover (LULC) will undergo influences or adjustments at various stages, ultimately affecting agricultural land in the impact region. It also becomes evident that areas with limited land use and land cover (LULC) extensions exhibit lower Water Productivity Index (WPI), primarily due to their reliance on agricultural land. It is observed that alterations, reductions, or modifications in LULC lead to changes in multiple aspects of agricultural land, resulting in noticeable variations in various metrics. The present paper not only evaluates the land use in the Upper Tons River Basin spanning from 2001 to 2021 but also highlights the changing patterns that impact water resources and their utilization capacity. Furthermore, the study estimates the influence of reducing specific features on the distribution of WPI and other LULC parameters. The Upper Tons River Basin faces challenges such as unfavorable rainfall patterns and inadequate planning for irrigation at the fundamental and local levels. Additionally, its geographical location in a rainfed area negatively affects the WPI.

Drought presents a substantial threat to both ecological and agricultural systems. Agriculture in Iraq is predicated on precipitation, which is a major contributor to the likelihood of drought resulting from even marginal fluctuations in precipitation. Furthermore, research suggests that Iraq suffers an approximate annual loss of 100,000 acres of arable land due to drought. NDVI and VCI, two significant indices, were utilized in this research to assess and monitor the severity of the drought in the northern region of Wasit province in Iraq. For the period from 1993 to 2023, drought intensity maps were generated utilizing NDVI-based VCI and the Geographic Information System (GIS), an extremely effective spatial data management instrument. NDVI results evidenced that the vegetation cover area was the highest in 1993 and 1998 and declined until it reached the lowest levels in 2023. The vegetation area was concentrated in the southwest parts. In contrast, VCI results demonstrated the extreme drought through the years from 2003 to 2023, which can be attributed to higher temperatures, evaporation, and lower amounts of rainfall. Throughout the thirty-year analysis period, extreme drought conditions were prevalent, especially in the last two decades. Furthermore, this drought should prompt the government to implement preventative measures to avert it. Implementing soil and water conservation measures, such as the establishment of percolation basins, contour bunds, and check dams, can also enhance drought management.

This study examines the cultural livelihood of Kogi State and its effects on the environment. The study describes some of the cultural livelihood practices found in Kogi State, considering the contemporary condition of cultural livelihood and its effects on the environment. Secondary and primary data were employed, which include archives and internet search engines. Using a 4-stage sampling procedure, data were collected from a 120-person sample through an interview, field observation, a focus group discussion, and a questionnaire. Descriptive statistics using frequencies, percentages, and charts were used for the analyses. The results were compiled using the Statistical Package for Social Sciences (SPSS). Findings show that about 85% of the participants discovered crop farming, arable farming, weaving, blacksmithing, fishing, and festivals of harvest, such as the New Yam Festival, among others, as the predominant cultural livelihoods. The local farming implements were made of local materials, like stones and wood. They have indigenous crop production, protection, and harvest techniques. The farming tools were economical in terms of labor, affordability, and time savings in the subsistence farming system. The study discovered that cultural livelihoods are 4% very efficient and 56% on the verge of extinction. Analyses of the effect of cultural livelihood show that 78% have a high negative effect on the economic environment, 57% have a moderate negative effect on the social environment, 51% hurt the political environment, and 22% have a low negative effect on the political environment. The intervention of the various tiers of government with the cooperation of the various communities is needed for the provision of a conducive environment for the practice of cultural livelihood, particularly in the aspect of insecurity. Adequate provision of modern equipment, funding, and social welfare services is also recommended to enhance cultural livelihoods.

Tannery wastewater contains a significant amount of chemical compounds, including toxic substances. Due to the toxicity and negative environmental effects of these tannery effluents, mandatory treatment is necessary. The main objective of this study was to treat effluent from an artisanal tannery in the city of Fez (Morocco) using the adsorption process with activated charcoal derived from olive pomace. The physicochemical characterization of tanning water included several parameters, such as chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), suspended solids (SS), sulfate ions (SO42-), nitrate, and chromium Cr(VI). The analyses show that the adsorption process reduced nitrate by 57.54%, sulfate by 94.08%, TKN by 74.84%, COD by 68.18%, Cr by 91.27%, and Cr (VI) by 89.78%. The activated charcoal was characterized before and after tannery effluent treatment using various techniques, including FT-IR, SEM, and EDX. From the above, it can be inferred that using activated carbon made from olive pomace has the potential to reduce tannery effluent pollution parameters. This innovative approach demonstrates that competitive results can be achieved without sacrificing economic viability, thereby promoting sustainable practices in the treatment of industrial liquid waste and wastewater treatment plants.

As part of green economics, taxes are imposed on emissions of pollutants that adversely impact the environment and public health to reward more innovative, environmentally sustainable, and low-carbon resource use. There are still many nation-states testing the concept of green taxation. Many environmental performance indicators place India low on the list of countries with the worst pollution. One of the main sources of pollution is vehicle exhaust. Green taxes will be imposed on older motor vehicles under guidelines released by the Indian government in 2021. The United Nations Framework Convention on Climate Change received the Indian Nationally Determined Contribution Report in 2022. Taxonomies and low-carbon transport systems were prioritized in India, and incentives and tax breaks were offered to encourage the manufacture and use of vehicles that consume more ethanol. Academic discussions and literature on the subject are still lacking among the masses. Researchers intend to analyze the legal and economic measures taken by the Indian Government to curb vehicular pollution against this background. Due to its significant contribution to air and water pollution, as well as greenhouse gas emissions, the automobile industry has come under increasing scrutiny in recent years. India and China, for instance, have implemented green tax policies to reduce the automotive sector’s environmental footprint and promote environmental sustainability. These policies are effective, but not all of them address the disproportionate impact of environmental injustice on vulnerable populations. Specifically, this study examines the impact of Indian green tax policies on environmental justice in the automobile industry as compared to those in China. A key aim of this study is to provide insights into the strengths and weaknesses of the green taxation policies adopted by each country in the automotive sector, as well as their implications for achieving environmental justice, by analyzing the scope, enforcement, impact on vulnerable communities, industry implications, and alignment with international commitments.

This study aims to design a community-based plastic waste management model specifically for Bangun Village, Mojokerto. Using a qualitative approach through a detailed case study, we gathered rich data from observations, interviews, and document reviews. Our findings reveal that the plastic waste management situation in Bangun Village is fraught with significant social, economic, and environmental challenges. These include inadequate waste segregation, limited recycling facilities, and a general lack of community awareness and participation. The proposed model seeks to address these issues by implementing several key components: community-based plastic waste collection and processing, educational programs to raise awareness and promote sustainable practices, partnerships with external stakeholders such as local government bodies, NGOs, and private sector entities, and institutional restructuring to support and sustain these initiatives. Central to this model is the belief that community education and awareness are crucial foundations for fostering sustainable behavior. By actively involving the community in the waste management process, the model not only aims to mitigate the plastic waste problem but also seeks to provide economic and social benefits to the residents of Bangun Village. This includes creating job opportunities, improving public health, and enhancing the overall quality of life. The strength of this model lies in its ability to integrate community participation, policy support, and external partnerships, making it a robust and effective solution for sustainable plastic waste management. By fostering a collaborative and inclusive approach, the model aims to create a sustainable and resilient community that can effectively tackle the plastic waste challenge while reaping economic and social benefits. In conclusion, the community-based plastic waste management model proposed for Bangun Village has the potential to bring about significant positive changes in the way plastic waste is managed. Through this model, we hope to empower the community to contribute to solving the plastic waste problem while also benefiting economically and socially.

The current research aims to optimize the solvent-based oil extraction process from Mahua (Madhuca indica) seed using response surface methodology and biodiesel production using heterogeneous catalysts. The oil extraction was varied through the levels of process parameters including extraction temperature (60 to 80°C), solvent-to-seed ratio (3 to 9 wt/wt), and time (2 to 4 h). The experiments were designed following the Central Composite model. The regression model provided optimal values for the selected process parameters based on the extraction yield percentage. To ensure the model’s reliability, it was experimentally validated. Maximum experimental oil yields of 50.9% were obtained at an optimized extraction scenario of 70 °C extraction temperature, solvent-to-seed ratio of 6 wt/wt, and time 4 h. The extracted oil’s physicochemical properties and fatty acid composition were tested. Also, using copper-coated dolomite as a catalyst, the extracted oil was transformed into biodiesel via transesterification. The FAME (94.31%) content of the prepared biodiesel was determined via gas chromatography. As a result, the findings of this study will be useful in further research into the use of Madhuca indica as a potential feedstock for biodiesel production.

Traffic accidents remain a pressing public safety concern, with a substantial number of incidents resulting from drivers' lack of attentiveness to road signs. Automated road sign recognition has emerged as a promising technology for enhancing driving assistance systems. This study explores the application of Convolutional Neural Networks (CNNs) in automatically recognizing road signs. CNNs, as deep learning algorithms, possess the ability to process and classify visual data, making them well-suited for image-based tasks such as road sign recognition. The research focuses on the data collection process for training the CNN, incorporating a diverse dataset of road sign images to improve recognition accuracy across various scenarios. A mobile application was developed as the user interface, with the output of the system displayed on the app. The results show that the system is capable of recognizing signs in real time, with average accuracy for sign recognition from a distance of 10 meters: i) daytime = 89.8%, ii) nighttime = 75.6%, and iii) rainy conditions = 76.4%. In conclusion, the integration of CNNs in automated road sign recognition, as demonstrated in this study, presents a promising avenue for enhancing driving safety by addressing drivers' attentiveness to road signs in real-time scenarios.