The Santurbán paramo is a sensitive high-mountain ecosystem exposed to pressures from extractive and agricultural activities, as well as increasing tourism. In response, this study presents a context-aware recommendation system designed to support sustainable tourism through the integration of deep neural networks and ontology-based semantic modeling. The proposed system delivers personalized recommendations—such as activities, accommodations, and ecological routes—by processing user preferences, geolocation data, and contextual features, including cost and popularity. The architecture combines a trained TensorFlow Lite model with a domain ontology enriched with GeoSPARQL for geospatial reasoning. All inference operations are conducted locally on Android devices, supported by SQLite for offline data storage, which ensures functionality in connectivity-restricted environments and preserves user privacy. Additionally, the system employs geofencing to trigger real-time environmental notifications when users approach ecologically sensitive zones, promoting responsible behavior and biodiversity awareness. By incorporating structured semantic knowledge with adaptive machine learning, the system enables low-latency, personalized, and conservation-oriented recommendations. This approach contributes to the sustainable management of natural reserves by aligning individual tourism experiences with ecological protection objectives, particularly in remote areas like the Santurbán paramo.

The main goal of this paper was to propose a program to develop sustainable tourism at Santurbán moor in Colombia. This would open new paths toward economic growth for the communities inhabiting this sector who are currently facing a serious dilemma. First, the moor is an area of vital importance to the sustainability of more than two million people, who depend on water generated in the area. On the other hand, this land contains great mineral wealth and agricultural resources. This has generated an already long-lasting conflict of interest between environment conservation and the possible economic exploitation of the Santurbán moor through industrial mining. To this end, we conducted bibliographic research on sustainable tourism, specifically with scientific ends, that considered the potential of this area for its implementation. Prospective methodology was applied, beginning with the selection of a panel of experts to identify the most important external and internal variables that could affect the area. Then, Impact Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) software was used to identify the correlation between the different actors and their possible contributions. As a result of this research, a proposal is presented that is focused on the needs of the community living in the area, as supported by science and the academic community, and by the use of Industry 4.0 and related new technologies. This strategy could be used in other protected areas in Colombia, or throughout the world, that are currently being visited by tourists who, maybe unwillingly, are actually putting the sustainability of these areas at risk. In the future, this strategy will be consolidated and will lead to technological applications. This will allow tourists to enjoy protected places without threatening the conservation of these habitats.

Natural reserves, such as the Santurbán Moor in Colombia, are ecologically important but face significant threats from activities like mining and agriculture. Preserving biodiversity in these ecosystems is essential for maintaining ecological balance and promoting sustainable tourism practices. Identifying plant species in these reserves accurately is challenging due to environmental variability and species similarities, complicating conservation efforts and educational tourism promotion. This study aims to create and assess a mobile application based on deep learning, called FloraBan, to autonomously identify plant species in natural reserves, enhancing biodiversity conservation and encouraging sustainable and educational tourism practices. The application employs the EfficientNet Lite4 model, trained on a comprehensive dataset of plant images taken in various field conditions. Designed to work offline, the application is particularly useful in remote areas. The model evaluation revealed an accuracy exceeding 90% in classifying plant images. FloraBan was effective under various lighting conditions and complex backgrounds, offering detailed information about each species, including scientific name, family, and conservation status. The ability to function without internet connectivity is a significant benefit, especially in isolated regions like natural reserves. FloraBan represents a notable improvement in the field of automated plant identification, supporting botanical research and efforts to preserve biodiversity in the Santurbán Moor. Additionally, it encourages educational and responsible tourism practices, which align with sustainability goals, providing a useful tool for both tourists and conservationists.

The present paper describes the main characteristics and components of a tool developed for integrating the definition of profiles for semantic Web services. This tool is based on the languages DAML-S and OWL-S. It includes the ontology visualization and consistency verification which specifies the concepts that a Web service interacts with. Starting from a service description interpreted by a computer and by the means used for accessing the service, it is possible to discover which software agents use the service. The tool can generate information in different formats to represent graphic information based on XML, such as the SVG format proposed by the W3C. The tool was developed in Java language. It is being used for the visualization of ontologies and for the semantic description of services in traffic information systems. The LISITT group (Laboratorio Integrado de Sistemas Inteligentes y Tecnologias de la informacion de Trafico) developed this tool at the Robotics Institute at the University of Valencia.

This paper studies the possibility of classifying coffee beans by using their features of color, shape and size. The process of acquiring the images were done in controlled lighting conditions. Based on segmented images, color, shape and size provide information about the physical alteration in green coffee beans, during the growing and drying process that affect the flavor and taste of the drink by Developing a computer program to sort coffee beans by types In the case of color images are analyzed in RGB space, which were preprocessor in order to reduce noise and separate from the background and enhance its features. The location was used for an algorithm for identifying and setting contours ellipse, an algorithm Mahalanobis distance classifier, an algorithm called Flood of growth, for the segmentation of those defective grains. The emergence of methods that take advantage of technological advances is an option whose benefits encourage the study of new possibilities. The classification of coffee beans throughout the analysis of images is a very promising because it is a minimally invasive method and the exposure of the grains are not visible to look deteriorates significantly. Many farmers sort their coffee beans by hand but only a few thousand dollars invested in a automatic sorting optical machine such us Sortex or Xeltron. In this paper we develop algorithms based on image processing techniques for the classification of defective beans

The sustainable management of natural reserves is increasingly prioritized within the global tourism sector, especially in fragile ecosystems like the Santurbán Moorland in Colombia. As a high-altitude Andean ecosystem providing essential water resources, the Santurbán Moorland faces mounting pressures from tourism growth and mining activity. This study assesses the adoption of sustainable tourism practices among tourism service providers (TSPs) in the region and identifies key gaps to inform policy and academic interventions. A cross-sectional, mixed-methods approach was applied, integrating surveys based on the European Tourism Indicators System (ETIS) and the Global Sustainable Tourism Council (GSTC) criteria, as well as structured interviews, field observations, and document analysis. Confirmatory factor analysis identified “sustainable management” as the most robust dimension (Cronbach’s alpha = 0.953); however, no TSPs reported using renewable energy, and less than 5% of employees had received formal training in tourism. The main challenges include the lack of environmental certification, insufficient infrastructure, and limited communication of sustainability practices. Based on these findings, the study proposes targeted public policies, financial incentives, and specialized academic training to strengthen sustainable practices. The results offer insights into the challenges faced by emerging ecotourism destinations and provide strategic guidelines to support a balance between environmental conservation and local socioeconomic development.