Machine learning (ML) has revolutionized resource management in agriculture by analyzing vast amounts of data and creating precise predictive models. Precision agriculture improves agricultural productivity and profitability while reducing costs and environmental impact. However, ML implementation faces challenges such as managing large volumes of data and adequate infrastructure. Despite significant advances in ML applications in sustainable agriculture, there is still a lack of deep and systematic understanding in several areas. Challenges include integrating data sources and adapting models to local conditions. This research aims to identify research trends and key players associated with ML use in sustainable agriculture. A systematic review was conducted using the PRISMA methodology by a bibliometric analysis to capture relevant studies from the Scopus and Web of Science databases. The study analyzed the ML literature in sustainable agriculture between 2007 and 2025, identifying 124 articles that meet the criteria for certainty assessment. The findings show a quadratic polynomial growth in the publication of articles on ML in sustainable agriculture, with a notable increase of up to 91% per year. The most productive years were 2024, 2022, and 2023, demonstrating a growing interest in the field. The study highlights the importance of integrating data from multiple sources for improved decision making, soil health monitoring, and understanding the interaction between climate, topography, and soil properties with agricultural land use and crop patterns. Furthermore, ML in sustainable agriculture has evolved from understanding weather data to integrating advanced technologies like the Internet of Things, remote sensing, and smart farming. Finally, the research agenda highlights the need for the deepening and expansion of predominant concepts, such as deep learning and smart farming, to develop more detailed and specialized studies and explore new applications to maximize the benefits of ML in agricultural sustainability.

Research on organizational culture and teamwork is progressing substantially, and the interest in understanding culture as an organizational phenomenon and its links with other variables of organizations are perspectives that need to be considered to obtain a better understanding and knowledge of achieving organizational aims. The objective of this study was to analyze the research and its contributions to organizational culture and teamwork through indicators of quantity, quality and structure. The information was obtained from the Scopus and Web of Science databases through structured search equations. The bibliographic data were merged and processed using Microsoft Excel, and by applying and using the PRISMA 2020 statement, 836 records were obtained. VOSviewer software was used to map networks, overlay displays and analyze keyword densities. The findings revealed that organizational culture and teamwork are constantly expanding and being researched in different organizations, with studies relating organizational culture and teamwork to knowledge management, patient safety culture, knowledge exchange, interprofessional collaboration and innovation. The maximum number of publications on the topic occurred in 2019. As indicated by VOSviewer, the keyword communication was associated with three of the identified clusters, and the research agenda proposed the expanding of themes related to leadership, knowledge exchange, knowledge management, interprofessional collaboration and safety climate.

In the field of art, machine learning models have been used to predict artistic styles in paintings. The foregoing is somewhat advantageous for analysts, as these tools can provide more valuable results and help reduce bias in the results and conclusions provided. Therefore, the objective of this research was to examine research trends in the use of machine learning to predict artistic styles from a bibliometric review based on the PRISMA methodology. From the search equations, 268 documents were found, out of which, following the application of inclusion and exclusion criteria, 128 documents were analyzed. Through quantitative analysis, a growing research interest in the subject is evident, progressing from user perception approaches to the utilization of tools like deep learning for art studies. Among the main results, it is possible to identify that one of the most used techniques in the field has been neural networks for pattern recognition. Also, a large part of the research focuses on the use of design software for image creation and manipulation. Finally, it is found that the number of studies focused on contemporary modern art is still limited, this is due to the fact that a large part of the investigations has focused on historical artistic styles.

This work aimed to identify the main variables that determine the adoption of Fintech services in young students in the Colombian context through a model with five factors proposed to explain this behavior in 124 Colombian university students. The methodological design followed a quantitative approach and an exploratory–descriptive scope. For data processing, the statistical techniques exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to extract the relevant factors and evaluate the measurement model. To test the hypotheses about the relationships of the conceptual model constructs, Cramer’s V coefficient was used. The results showed that financial education and social influence have a positive effect on perceived benefit; in turn, low regulation is not strongly related to perceived benefit and is not dependent on social influence. However, digital literacy is affected by financial education and social influence. It is concluded that the number of mobile users in Colombia is increasing rapidly; however, the adoption of Fintech is slow. In addition, most of the university students in this study do not know what Fintech is, but they recognize that they use it frequently.

The present study explores the potential of Artificial Intelligence (AI) in driving the sustainable economy forward through the optimization of resources and the mitigation of environmental impacts. However, the study also identifies significant challenges in the conceptual and methodological integration necessary to establish a comprehensive theoretical framework. The heterogeneity of these approaches hinders the discernment of discernible patterns within the scientific literature, thereby impeding the development of efficacious strategies to optimize their impact on sustainability. The present study employs the PRISMA 2020 methodology to analyze trends in scientific production on AI and the sustainable economy, thereby ensuring a rigorous process of identifying and evaluating studies. The mapping of the academic community, the geographical distribution of knowledge, and the evolution of approaches allow us to understand the research dynamics and identify gaps in the literature. This study makes a significant contribution to the field by offering a comprehensive perspective that supports the integration of AI into sustainable models and its application in sectors with high environmental and economic impacts.