Esta investigación consiste en una revisión literaria de publicaciones científicas en el área de la inteligencia artificial (IA), pertenecientes a revistas científicas encontradas en el portal SCImago Journal & Country Rank. La búsqueda de información se realizó utilizando palabras clave y títulos de investigaciones publicadas entre los años 1970 y 2020 en la base de datos de Scopus. El objetivo de este artículo es identificar los aportes de la IA en la educación en las últimas cinco décadas, dara conocer las revistas científicas con los índices de impacto más altos en el área de la IA en los últimos 10 años, y analizar el papel que desempeñará la IA en la educación post Covid-19. Los resultados evidencian aportes significativos de la IA en la educación, empleando técnicas de redes neuronales, big data, visión por computador, asistentes digitales virtuales, aprendizaje automático y análisis predictivo, siendo Estados Unidos el país que posee el mayor número de revistas científicas (siete) dedicadas al área de la IA. Finalmente, destacamos la necesidad de involucrar la IA en el proceso de enseñanza y aprendizaje en una educación post Covid-19.

The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different hypsometric modeling techniques. Currently, machine learning techniques, especially artificial neural networks, have revolutionized modeling for forest management, obtaining more accurate predictions; it is because we understand that it is of the utmost importance to adapt, evaluate and apply these methods in this species for large areas. The objective of this study was to build and evaluate the efficiency of the use of a deep neural network for the prediction of the total height of Guazuma crinita Mart. from a large-scale continuous forest inventory. To do this, we explore different configurations of the hidden layer hyperparameters and define the variables according to the function HT = f(x) where HT is the total height as the output variable and x is the input variable(s). Under this criterion, we established three HT relationships: based on the diameter at breast height (DBH), (i) HT = f(DBH); based on DBH and Age, (ii) HT = f(DBH, Age) and based on DBH, Age and Agroclimatic variables, (iii) HT = f(DBH, Age, Agroclimatology), respectively. In total, 24 different configuration models were established for each function, concluding that the deep artificial neural network technique presents a satisfactory performance for the predictions of the total height of Guazuma crinita Mart. for modeling large areas, being the function based on DBH, Age and agroclimatic variables, with a performance validation of RMSE = 0.70, MAE = 0.50, bias% = −0.09 and VAR = 0.49, showed better accuracy than the others.

Traditional field inventories have been the standard method for collecting detailed forest attribute data. However, these methods are often time-consuming, labor-intensive, and costly, especially for large areas. In contrast, remote sensing technologies, such as unmanned aerial vehicles (UAVs), have become viable alternatives for collecting forest structure data, providing high-resolution images, precision, and the ability to use various sensors. To explore this trend, a bibliometric review was conducted using the Scopus database to examine the evolution of scientific publications and assess the current state of research on using UAVs to estimate dendrometric variables in forest ecosystems. A total of 454 studies were identified, with 199 meeting the established inclusion criteria for further analysis. The findings indicated that China and the United States are the leading contributors to this research domain, with a notable increase in journal publications over the past five years. The predominant focus has been on planted forests, particularly utilizing RGB sensors attached to UAVs for variable estimation. The primary variables assessed using UAV technology include total tree height, DBH, above-ground biomass, and canopy area. Consequently, this review has highlighted the most influential studies in the field, establishing a foundation for future research directions.

Research highlights: We used Dijkstra Algorithm (DA) to define optimal allocation of yards in order to minimize total skid-trail’s distance in the Amazon Forest. DA minimized trails’ distances and associated transportation costs, leading to an even smaller value when the current planning was disregarded and suggesting the reduction of deleterious environmental externalities. Background and objectives: We sought to answer if it is possible to optimize distances and intrinsic costs in the management of Amazonian forests using DA. The objective was to minimize skid trails distances by best allocating yards using DA and to compare four scenarios of forest harvest planning in the Brazilian Amazon. Materials and methods: Tree census data from Gênesis-Salém Farm, state of Pará, Brazil, were used. The yards and roads located by Grupo Arboris (scenario 1) were compared to three alternative scenarios in terms of total skid distance, trails and road densities, and skidding costs for three successive harvests, seeking to minimize total skid-trails’ distance. Alternative scenarios were to keep the number of yards within work units (WU) and place them in the edge of existing roads (scenario 2); keep the number of yards within each WU (scenario 3); and place 23 yards, disregarding the current planning (scenario 4). Results: Total skid-trail’s distance, number of trees above optimal extraction distance and densities of skid trails and roads were smaller in scenarios 2, 3, and 4, compared to the current yard allocation (scenario 1). Scenario 4, with fewer restrictions, reduced skid-trails’ distances by 23%. Harvest costs decreased from scenario 1 to 4 in all three harvest cycles. Conclusions: DA allowed optimized distribution of yards and skid trails and generated efficient results for harvest planning. This reinforces the importance of optimized planning, which establishes satisfactory results in the effort to reduce costs and environmental impact keeping high efficiency.