Forests are of incalculable value due to the ecosystem services they provide to humanity such as carbon storage, climate regulation and participation in the hydrological cycle. The threat to forests grows as the population increases and the activities that are carried out in it, such as: cattle rearing, illegal trafficking, deforestation and harvesting. Moreover, the environmental authorities do not have sufficient capacity to exercise strict control over wood production due to the vast variety of timber species within the countries, the lack of tools to verify timber species in the supply chain and the limited available and labelled digital data of the forest species. This paper presents a set of digital macroscopic images of eleven tropical forest species, which can be used as support at checkpoints, to carry out studies and research based on macroscopic analysis of cross-sectional images of tree species such as: dendrology, forestry, as well as algorithms of artificial intelligence. Images were acquired in wood warehouses with a digital magnifying glass following a protocol used by the Colombian Ministry of Environment, as well as the USA Forest Services and the International Association of Wood Anatomists. The dataset contains more than 8000 images with resolution of 640 × 480 pixels which includes 3.9 microns per pixel, and an area of (2.5 × 1.9) square millimeters where the anatomical features are exposed. The dataset presents great usability for academics and researchers in the forestry sector, wood anatomists and personnel who work with computational models, without neglecting forest surveillance institutions such as regional autonomous corporations and the Ministry of the Environment.

Academic training of professionals, especially in engineering fields, should enable them to reliably interpret their surroundings for informed decision-making. This is crucial for engineering students who continuously quantify and anal-yze system operations. In the current context, where in-person and virtual work blend together thanks to technological advances, professional education must establish flexible alternatives to assimilate phenomenological knowledge without sacrificing essential aspects of real experimentation. Remote laboratories emerge as an alternative to strengthen these aspects through distance learning. This paper presents a proposed and developed functional architecture scheme for remote interaction-based mechanical physics laboratories, allowing the configuration of initial operating conditions, execution of experiments, and data collection for three fundamental physics practices: free fall, Hooke’s law, and parabolic motion. The proposed architecture is scalable under the Internet of Things approach, using the necessary hardware and software elements with user-friendly interaction interfaces, making the developed remote laboratories a viable option for engineering students’ education, without requiring physical presence for their execution.

Monitoring and controlling illegal timber trafficking remains a formidable global challenge. The timber sector faces this issue without practical and on-site support systems to facilitate these tasks, and there exists a limited availability of technological and automated tools to assist control personnel in fulfilling their responsibilities. The challenge intensifies in regions where workers possess inadequate expertise in confidently identifying the forest species involved in illegal trade. This paper introduces the architectural framework and a computational model for a digital support tool designed to recognize twenty timber species that are illicitly traded in the Colombian Amazon region. A lightweight convolutional neural network was trained using the transfer learning approach and an in-house generated dataset. The resulting model was deployed on the cloud, following Software as a Service principles, and on a portable embedded system. The prototype exhibits a classification performance exceeding 93%, successfully emulating real-world conditions in the field, including challenges such as imprecise cutting techniques, low-resolution image capture devices, and images captured at varying orientations. Furthermore, the classifier model has been incorporated into a chatbot and a low-cost microcomputer, enabling rapid responses in less than ten seconds. This integration enhances versatility, reduces the subjectivity of the identification process, supports both online and offline operation, and offers potential scalability for the entire system.