Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km² in built-up areas. A net decrement of 75.44 km² was observed in forest areas. A drastic net decrease of 674.52 km² was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.

The objective of this study was to develop a model based on fuzzy rules to evaluate the effects caused by varying the dosages of two soil fertilizers (mineral and organic) on root diameter. Fuzzy logic is a method that presents a language, more appropriate to day-to-day life, as the sky is a bit cloudy. For the input variables of this system the mineral and an organic fertilizer were used, for the output the root diameter, in cm. After optimization of the input rules, it can be seen that for the application of the fertilizers (mineral and organic) the best dosages were from 15 to 60 and 20 to 60 g·m^-2, respectively. With this application of fuzzy rules in real data, it is possible to take these benefits to those involved in the production chain of radish, resulting in a reduction in the dosages of products and improving its final profitability.

Unmanned Aerial Vehicles (UAVs) have gained spotlighted attention in the recent past and has experienced exponential advancements. This research focuses on UAV-based data acquisition and processing to generate highly accurate outputs pertaining to orthomosaic imagery, elevation, surface and terrain models. The study addresses the challenges inherent in the generation and analysis of orthomosaic images, particularly the critical need for correction and enhancement to ensure precise application in fields like detailed mapping and continuous monitoring. To achieve superior image quality and precision, the study applies advanced image processing techniques encompassing Fuzzy Logic and edge-detection techniques. The study emphasizes on the necessity of an approach for countering the loss of information while mapping the UAV deliverables. By offering insights into both the challenges and solutions related to orthomosaic image processing, this research lays the groundwork for future applications that promise to further increase the efficiency and effectiveness of UAV-based methods in geomatics, as well as in broader fields such as engineering and environmental management.

With its inherent characteristics of decentralization, immutability, and transparency, blockchain technology presents a promising opportunity to revolutionize the South African food supply chains. Blockchain technology, with its decentralized, immutable, and secure nature, offers solutions to these challenges by improving traceability and accountability across the supply chain. This study investigates the role of blockchain technology in enhancing transparency in the food supply chain among small and medium enterprises in South Africa. SMEs form a critical part of the country’s agri-food sector but face challenges such as food fraud, inefficient inventory management, and lack of transparency, which impact food safety and trust. The research adopts a mixed-method approach, utilizing the Technology-Organization-Environment framework and Institutional Theory to explain blockchain adoption among SMEs. The results demonstrate that blockchain-enabled practices, such as smart contracts, records traceability, production tracking, and distribution monitoring, significantly enhance supply chain transparency. The findings highlight blockchain’s potential to increase operational efficiency, regulatory compliance, and stakeholder trust. This research provides valuable insights for policymakers and practitioners, emphasizing the need for regulatory support and strategic investment in blockchain solutions to promote sustainability and competitiveness in the agri-food sector.

Introduction: Citizen insecurity is a complex, multidimensional and multi-causal social problem, defined as the spaces where people feel insecure mainly due to organized crime in all nations that suffer from it. Objective: To analyzes the sociodemographic factors associated with public insecurity in a Peruvian population. Methodology: The research employed a non-experimental, quantitative design with a descriptive and cross-sectional approach. A total of 11,116, citizens participated, ranging from 18 to 85 years old (young adults, adults, and the elderly), of both sexes, and with any occupation, education level, and marital status. The study employed purposive non-probability sampling to select the participants. Results: More than 50% of the population feels unsafe, in public and private spaces. All analyzed sociodemographic variables (p < 0.05), showing distinctions in the perception of citizen insecurity based on age, gender, marital status, occupation, area of residence, and education level. It was determined that young, single students, who had not experienced a criminal event and reside in urban areas, regardless of gender, perceive a greater sense of insecurity. Contribution: The study is relevant due to the generality of the results in a significant sample, demonstrating that the study contributes to understanding how various elements of the socioeconomic and demographic context can influence the way in which individuals perceive insecurity in their communities, likewise, the perception of citizen insecurity directly affects the general well-being and quality of life of residents, influencing their behaviors and attitudes towards coexistence and public policies; which will help implement more effective actions in the sector to reduce crime rates.