Conference Agenda

Modern aquaculture faces significant challenges associated with continuous water quality monitoring, particularly in rural areas where electrical and telecommunications infrastructure is limited. This article presents the design, implementation, and experimental validation of a Remote Terminal Unit (RTU) based on the Internet of Things (IoT) and Long Range Wide Area Network (LoRaWAN) technology for the continuous monitoring of critical water quality variables such as pH, dissolved oxygen, and temperature in aquaculture systems. The proposed solution integrates multi-parameter sensors, a low-energy consumption architecture, and both local and remote configuration and calibration mechanisms. Data is transmitted via LoRaWAN to The Things Network, enabling real-time monitoring of pond biochemical conditions under variable environmental scenarios. Experimental validation was conducted in real aquaculture ponds, comparing RTU measurements with certified reference equipment; results demonstrated high correlation, with maximum deviations of less than 0.3 pH units, ≤0.2 mg/L for dissolved oxygen, and within ±1 °C for temperature. Field tests further confirmed reliable long-distance communication and reduced energy consumption, ensuring viability for prolonged autonomous operation. This robust architecture contributes to aquaculture productivity sustainability and aligns with Sustainable Development Goals related to efficient water management, food security, and technological innovation.

In a context of growing demand and increasingly limited resources, agribusiness faces the urgent challenge of evolving toward more regenerative and socially responsible production models. The objective of this article was to analyze the synergy between the Circular Economy (CE) and Industry 5.0 (I5.0) to enhance sustainability in the agribusiness sector. The methodological strategy consisted of a Systematic Literature Review. Using the PICO approach and the PRISMA protocol, an exhaustive search was conducted in Scopus and Web of Science, identifying 863 records, of which 20 met the inclusion criteria. The results highlight that the convergence between CE and I5.0 promotes more efficient use of resources, reduces environmental impacts through optimized processes and smart technologies, and favors social benefits associated with well-being, occupational safety, and the strengthening of organizational culture. Likewise, challenges were identified, such as limited technological adoption, high initial costs, and a scarcity of empirical evidence applied to the sector. The study concludes that the articulation between CE and I5.0 constitutes a strategic opportunity to move towards a more resilient and sustainable agribusiness, underscoring the need to strengthen applied research and institutional frameworks to achieve its effective implementation.

To address the scarcity of drinking water in rural areas and regions with extreme climates, an autonomous rainwater and hail harvesting system powered by solar energy was developed. This design was based on a survey conducted with people in southern Peru to understand their water habits and needs. The system includes a stainless-steel collection cylinder with thermal resistors activated by temperature sensors to melt the hail, and sensors for rainfall, flow, and water level, all managed by a control box. The collected water is filtered and stored in a modular tank, and the entire operation is automated and monitored in real time via a web-based platform accessible via the internet. Its components are protected in a dust- and moisture-resistant cabinet. This low-maintenance and energy-efficient solution represents a practical and replicable alternative to traditional water harvesting methods, benefiting communities with limited access to this resource

Abstract– This study examines the smart canes that have emerged in recent years to support people with visual impairments. The study aimed to evaluate and compare the effectiveness of three main technologies in smart canes for people with visual impairments: ultrasonic, artificial intelligence (AI), and hybrid systems. The methodology involved a systematic review of literature published between 2020 and 2025. Eight independent searches were conducted in four databases, identifying 908 initial articles, which were reduced to 180 unique articles after duplicate removal. The following dimensions were evaluated: accuracy, detection range, user satisfaction, autonomy, response speed, and environmental robustness. The results showed that hybrid systems obtained the highest overall score (89.2/100), with 95% accuracy, a range of 0.3–9 m, and 93.5% user satisfaction. Ultrasonic systems achieved a score of 87.4/100, standing out for their low cost. Systems based solely on AI scored 80.1/100, limited by shorter autonomy and vulnerability in low-light conditions (60-65% performance). The study concluded that hybrid systems represent the state of the art in overall effectiveness, but ultrasonic systems demonstrate surprising competitiveness. There is no single optimal solution; the choice depends on the context of use, budget, and specific needs. Critical gaps were identified in longitudinal studies, evaluations in diverse real-world contexts, and rigorous economic analyses.

One of the main risks in homes is fire, which can be caused by gas leaks, electrical faults, or unattended appliances. In response to this, we propose a smart fire detection and alert system for homes in Arequipa. The system integrates temperature, gas, and motion sensors with an ESP32 electronic board to monitor environmental variables in real time. When a risk is detected, it activates visual and audible alarms and sends immediate alerts to the user's smartphone via Wi-Fi. The alerts include real-time information about the detected hazard, allowing for quick action even when no one is home. The system also offers scalability and affordability, making it ideal for local homes. In this research, a survey was conducted, in which 77.1% said they had faced dangerous situations in the kitchen, although without a fire breaking out. Likewise, 68.6% consider it very important to have a safety system, and the most common causes of incidents are related to a lack of supervision when cooking. These results support the relevance of the proposed system and reflect a real need.

The Pan American Health Organization reports that in Latin America, more than 28% of the population faces moderate or severe food insecurity, while nearly 58% of adults are overweight or obese. This research presents a proposal for a nutritional control prototype based on computer vision and embedded systems, implemented on a Raspberry Pi platform. The system integrates image capture, food classification algorithms, and weight sensors to estimate nutritional intake in a semi-automated manner, reducing dependence on manual recording of dietary information. The results of the cross-sectional study applied to young people between the ages of 12 and 24 in Arequipa show deficiencies in nutritional culture and food self-control practices, validating the relevance of the proposed device as a potential scalable engineering solution.