Conference Agenda

The accelerated development of computer vision (AM) worldwide has a significant impact on fruit harvest estimation, leading to increased efficiency and a notable reduction in crop waste. This technology faces notable resistance to its use due to lack of knowledge in the agricultural sector. The objective of the work is to analyze artificial vision methods in predicting the harvest of high-stemmed fruits. Based on the inclusion and exclusion criteria, 26 open access articles from the Scopus, Scielo and Redalyc databases were chosen. The findings highlight that most studies used near-infrared (NIR) spectroscopy and RGB image processing to estimate harvest, achieving accuracies of 95% (citrus fruits) and 75% (apples) on average respectively. The use of UAVs with RGB and YoloV3 image sensors made it possible to achieve accuracies greater than 90%, enabling predictions made between 4 and 6 months before the harvest. It was concluded that the most used VA methods were RGB image sensor, spectrometry (NIR), unmanned aerial vehicles (UAV) and YOLOv3, which demonstrated accuracies greater than 75% in predicting the maturity of high-stemmed fruits. The choice of method will depend mainly on whether you want to analyze the internal or external part of the fruit, or both.

Augmented reality has become an innovative tool with numerous potential applications, opening new horizons in human interaction with physical and digital environments. Initially focused on entertainment and education, its potential to transform the tourism sector has aroused great interest. AR can enrich tourist destinations with documentation, interactive guides, and historical entertainment, providing travelers with a unique and enriching perspective. The general objective of this research is to analyze the implementation of augmented reality in the promotion of tourism in Piura, evaluating its impact and effectiveness in improving the tourist experience and preserving cultural heritage. A systematic literature review was conducted using the Redalyc, Scielo, Dialnet, and Scopus databases. After applying inclusion and exclusion criteria, 30 relevant studies were selected for review. The results indicate that the implementation of AR can promote the socioeconomic development of Piura, offering a competitive advantage in the tourism market. However, it is concluded that pilot studies are essential to evaluate the practical implementation and long-term acceptance of AR in Piura. In addition, AR has significant potential to improve the tourism experience and promote the socioeconomic development of Piura. However, pilot studies are essential to evaluate the practical implementation and long-term acceptance of this technology.

Efficient solid waste treatment is a growing challenge for ever-expanding urban populations. One innovative solution is the application of artificial intelligence (AI), which seeks to improve the detection and categorization of waste in population settings. The aim of this review is to evaluate the effectiveness of AI algorithms, especially Deep Learning models, in the automated identification of urban solid waste in different population contexts. Using the PICO and PRISMA methodologies, a comprehensive research was carried out in scientific repositories such as Scopus, Dialnet and Redalyc. The keywords used included "solid waste", "AI", "Deep Learning", "solid waste" and "waste management". The findings suggest that Deep Learning-based models show promising performance in the detection and classification of solid waste in various urban settings, with accuracy rates exceeding 90% in some cases. It was observed that the effectiveness of these systems is highly dependent on the quality and diversity of the preparation data sets, which must reflect the variability of waste in different types of populations. The review concludes that AI has significant potential to achieve optimisation of solid waste treatment in local environments, although further research is required to address challenges such as variability in lighting conditions and sorting in complex environments.

This study conducts a systematic review of the literature on the influence of technology in solid waste management between 2019 and 2024. The search focused on the variables "technology" and "solid waste management" in the Scopus database, applying filters for criteria such as access, publication stage, time period, language, subject area, and keywords, selecting a reliable set of 19 articles. The results show that technology has a positive impact on solid waste management, reducing greenhouse gases, improving air and soil quality, and preserving natural resources through the application of automated systems, waste conversion processes, and recycling. The year 2023 recorded the highest number of publications, highlighting terms such as urban solid waste and sustainable development. India, Malaysia, and Italy lead in scientific production, suggesting that Latin America needs to conduct more research in this field. The studies demonstrate the potential of mathematical optimization, allocation and routing models, and geographic information systems (GIS) to improve efficiency and sustainability in solid waste management.

Blockchain emerges as an innovative technology with potential applications in the healthcare sector due to its demonstrated qualities of decentralization, distribution, and data integrity. This systematic literature review (SLR), without meta-analysis, aims to analyze current perspectives and trends in blockchain interoperability for information systems in the healthcare sector, focusing on challenges and opportunities to enhance healthcare data management through this technology. Using the PICO strategy and PRISMA methodology, 25 open-access articles from Scopus and PubMed databases were reviewed, addressing blockchain interoperability and healthcare between 2020 and 2024. The results highlight those perspectives on blockchain interoperability in healthcare focus on improving efficiency and security in data exchange through a decentralized network. Furthermore, trends indicate the use of platforms and standards such as FHIR, IPFS, Ethereum, and Hyperledger to facilitate this exchange. In conclusion, this study underscores blockchain's potential to transform health data management and exchange through cryptographic mechanisms that enhance the security and efficiency of information systems. It also identifies trends and the use of these platforms and standards that contribute to achieving interoperability.

The objective of this research was to determine the relationship between business perception and knowledge management for the use of virtual reality in companies in emerging countries. An applied methodology with a quantitative approach was used, at a correlational level and as a data collection instrument, a questionnaire was applied for each variable. The result of the study allowed us to identify a positive relationship between both variables. The conclusion of the study indicates that when there is a favorable business perception for the use of virtual reality, knowledge management will be a less complex and more optimal process for companies.