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This study is dedicated to exploring farmers’ perceptions of power weeders, a contemporary agricultural tool widely employed in diverse farming operations. It delves into the factors influencing farmers’ preferences for these machines and the tangible benefits they derive from incorporating them into their agricultural practices. To comprehensively capture farmers’ perspectives, the research employs qualitative data collection methods, such as in-depth interviews and focus group discussions. Through these methods, the study aims to glean the subjective experiences and opinions of farmers, providing a rich and nuanced understanding of their attitudes towards power weeders. The collected data undergoes rigorous analysis utilizing diverse statistical tools, including simple percentage analysis, weighted average rank tests, and factor analysis. The findings of the study highlight several compelling reasons behind farmers’ inclination towards power weeders. Notably, the tool’s versatility emerges as a key factor, allowing it to be utilized across different crop types. Farmers express a strong preference for the ease of operation, low fuel consumption, and user-friendly features associated with power weeders. Additionally, government subsidies are identified as a significant motivating factor driving the widespread adoption of these tools among farmers. Moreover, the study sheds light on the array of benefits farmers experience through the utilization of power weeders. These advantages encompass the tool’s ease of manoeuvrability, its role as a versatile land transport mechanism, potential enhancements in overall productivity, and its suitability for a wide range of crops. In essence, the research provides a comprehensive exploration of farmers’ positive perceptions and the tangible advantages that drive their preference for integrating power weeders into modern agricultural practices.

This review article titled "Precision Farming: The Power of AI and IoT Technologies" delves into the transformative impact of Artificial Intelligence (AI) and the Internet of Things (IoT) in revolutionising modern agricultural practices. This paper explores the integration of cutting-edge technologies, including AI, IoT, remote sensing, and Geographic Information Systems (GIS), in addressing the challenges posed by diminishing agricultural resources and an expanding population. Precision farming, empowered by these technologies, offers a paradigm shift from traditional farming methods to a more data-driven, efficient, and environmentally sustainable approach. The use of AI and IoT in agriculture has led to significant advancements in crop monitoring, resource management, and decision-making processes. However, the implementation of these technologies is not without challenges, such as accessibility, connectivity, and the complexity of integration. This article reviews the current state and future prospects of precision farming, emphasising its role in enhancing agricultural productivity while minimising environmental impact. The potential for future innovations in this field is vast, promising a new era of efficient and sustainable farming practices essential for global food security.

Papua New Guinea has plenty of land with suitable agro-climatic conditions for rice farming, but despite this, the country still needs to import rice to meet the demands of its rapidly growing population and urbanization. To address this issue, a research project was undertaken to create a realistic scope and map of areas within each province of Papua New Guinea that are suitable for rice production, using Remote Sensing (RS) and Geographic Information System (GIS) techniques. The study included nineteen regions of Papua New Guinea. A digital surface model was used to determine the suitability of each area for large-scale rice farming, based on climate, soil factors, and topography. The Erdas Imagine v-11 model builder was used to create a critical model for the suitability of rice mapping. Each province was classified into five classes based on the suitability of the land for rice farming. The results showed that approximately 14% of the land in Papua New Guinea is exceptionally suitable for rice farming. Eight provinces (Central, East Sepik, East New Britain, Morobe, Madang, Milne Bay, West New Britain, and Western Province) were identified as having large-scale land suitable for rice production and were evaluated accordingly.

In recent days, Indoor Air Quality (IAQ) has become a matter of interest for researchers from both scientific and industrial community. During pandemic situation public settle more time at indoor in place of outdoor environment. With rapid growth in science and technology people have progressive necessity for active facility parameters with respect to indoor temperature, humidity and wind movement etc. Asthma, lung cancer, hypertension, cardiovascular issues, and respiratory illnesses are just a few of the chronic health issues that have been linked to frequent exposure to hazardous material in enclosed spaces. However, real-time monitoring technologies are being adopted in smart city projects to identify and remedy such unfavourable conditions, ultimately improving the general quality of life. This paper surveyed the current proposed IAQ monitoring systems in terms of Internet of Things (IoT). In addition, the research work highlights composition of diverse monitoring systems, research summary, challenges together implying future directions for the researchers. The current level of knowledge on the close relationship between IoT and IAQ is presented in this research.

This review article, “Smart Agriculture Solutions: Harnessing AI and IoT for Crop Management,” explores the transformative impact of digital technologies in agriculture. It focuses on the integration of the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) in enhancing sustainable farming practices. IoT technologies enable real-time monitoring of agricultural conditions using sensors and drones, while Cloud Computing provides robust data processing capabilities. AI leverages this data for predictive analytics in crop yields, disease detection, and automation of farming processes, optimizing resource use and adapting to climate change. The paper also examines the synergy of renewable energy sources with AI to power smart farming systems, proposing innovative solutions for energy-efficient, AI-driven agriculture. It highlights AI’s extensive role in improving crop management, from seed selection to soil analysis and crop health monitoring, thereby increasing yield quality and fostering sustainability. Furthermore, the article discusses the significance of space technologies in smart agriculture, showcasing advancements like drones and big data analytics. These innovations are crucial in developing economies, addressing global challenges like food security and contributing to economic growth. This review underlines the necessity of AI and IoT in revolutionizing agricultural methodologies, ensuring efficiency and sustainability in the face of a rapidly growing global population.

— Lithological mapping is a crucial factor in identifying and mapping the spatial distribution of minerals. It aids in accurately defining the most promising primary prospects for local exploration. The differentiation of rock units across a wider region is likely to be attributed to remotely sensed satellite data. Therefore, the research focuses on utilizing remote sensing methods to create a geological map for a specific area in Salem district, Tamil Nadu, by employing HYPERION and ASTER satellite images. Various techniques, such as Band Ratio (BR), Spectral Angle Mapper (SAM), Minimum Noise Fraction (MNF), Mixture Tuned Mapped Filtering (MTMF), Spectral Feature Fitting (SFF), and Support Vector Machines (SVMs), are utilized to classify lithological units, which are crucial for data analysis. The outcomes of these methods will be compared to field-mapped geological boundaries to assess accuracy. In the final phase, a highly precise geological map is produced by combining remote sensing data with on-site investigations. The application of these approaches holds significant potential for enhancing geological mapping and mineral exploration in hard-to-reach areas.

Forest fires are a type of natural catastrophe that poses a risk to the vegetation and fauna of the hill stations. Due to the presence of vast enormous areas of land adorned with aged trees, vegetation, and wild life, safeguarding the ecosystem is both critical and arduous. A Geographic Information System and Remote Sensing assist in resolving this issue through the continuous monitoring of the forest using satellite, aerial, and drone-based imagery gathered from a variety of sources in India and Abroad. Landsat8 series Band 4, Band 5, Band 6 and Band 7 is used for the study. Following the correction and analysis of reflectance values for pre-fire and post-fire imagery, the Normalized Burn Ratio (NBR) is computed and processed. Finally, the difference in NBR based on the pre-fire event and post-fire event for the Yercaud hill station is analyzed and the severity level is classified for the chosen area of interest.

The most current of all disruptive technologies is artificial intelligence (AI), which has tremendous potential to transform marketing. AI is also the latest of all disruptive technologies. Professionals are scrambling to decide which artificial intelligence solutions will be the most advantageous for the specific projects they are working on. On the other hand, an exhaustive review of the relevant literature might highlight the significance of AI and serve as a roadmap for future study. This study on artificial intelligence (AI) aims to comprehensively analyze AI by doing a bibliometric, conceptual, and intellectual network analysis of the academic literature on the topic. Following a rigorous analysis of the publications, the most influential authors in the scientific community as well as the most trustworthy resources, were uncovered. The co-citation and co-occurrence analyses, respectively, helped along the building of the theoretical and philosophical network. Data clustering was beneficial in identifying study subthemes and future research subjects, which was done in order to improve the usage of artificial intelligence.