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.

According to the World Health Organization, 80% of adolescents do not meet the minimum recommended levels of physical activity, a situation exacerbated by excessive screen exposure and increasingly sedentary lifestyles. This trend has contributed to a progressive decline in visuomotor skills that are essential for functional development. In response, this study presents the development of ENVISO, a low-cost bilateral visuomotor training system based on an ESP32 microcontroller, capable of generating dynamic visual stimuli and recording real-time motor responses. Its portability, modular architecture, and ergonomic interface allow for implementation in school, community, or rehabilitation settings with minimal infrastructure.A total of 26 young adults participated in an intervention using five training modes. The Timed Reaction modality, which presents random stimuli with a 750 ms response window, was identified as the most effective. After four weeks of training (two sessions per week), participants showed an average reduction of 68.16 ms in reaction time and improved neuromotor consistency, with no significant differences across body mass index groups (p = 0.169). The system provides immediate performance feedback via an integrated LCD screen, without requiring external software.