Conference Agenda

This study was conducted in Cajamarca, focusing on milk storage tanks and simulating them using CADE SIMU and ProModel. The overall objective was to simulate an automated level monitoring and notification system using Arduino and ultrasonic sensors. The methodology consisted of a quasi-experimental simulation-based design, building the electronic model in CADE SIMU and the filling process in ProModel, analyzing time scenarios, and exporting data to Excel. The results, according to the specific objectives, demonstrated: problems with manual control such as overflows and weekly losses; 97% accuracy of the ultrasonic sensor in the simulation; and an 85% reduction in overflows and an 87% reduction in losses with the automated system. In conclusion, it was validated that automation improves storage efficiency, reduces human error, optimizes filling times, and proves to be a viable and effective solution for the dairy industry in Cajamarca.

This study presents the design and implementation of an automated system for the efficient sorting of Persea americana (avocado) in the city of Cajamarca, Cajabamba province, Condebamba district. The objective is to replace the traditional manual process, which is characterized by high variability, errors due to visual fatigue, and high operating costs. The research was conducted in four phases: diagnosis of the manual process, design of the automated prototype based on machine vision and mechanical control, comparative performance evaluation, and economic analysis. The proposed system integrates a conveyor belt, ultrasonic sensors, and pneumatic actuators, allowing the fruit to be sorted according to size, ripeness, color, and surface defects. The results show significant improvements: a 58.7% reduction in processing time, a 79.7% decrease in errors, a 96.2% increase in accuracy, and a 142.4% increase in production capacity. The economic analysis demonstrates a payback period of 1.29 years and a 49.7% reduction in operating costs. It is concluded that automation is technically and economically viable, improving the competitiveness of the agro-industrial sector and aligning with the principles of Agriculture 4.0.

This work presents the design, simulation and validation of a low-cost temperature monitoring system based on Arduino and the DHT-11 sensor, oriented to the cold chain in small cheese factories in Cajamarca dedicated to the production of buttery cheese. Faced with the problem of precise and continuous thermal control, which affects the quality and safety of the product, a prototype was developed that records temperatures in real time, visualizes data on an LCD screen and activates automatic alerts in the event of critical deviations. The methodology included selection and characterization of the DHT-11 sensor (range 0-50°C, accuracy ±0.5°C, low cost), construction of the electronic circuit with accessible components, programming for data logging and alerts, and evaluation in a simulated environment. The results demonstrated significant quantitative improvements: reduction of the margin of error by 80%, increase in measurement frequency by 1,800-5,400 times and decrease in the risk of cold chain breakdown by 90%, with economic savings of 60-70% and stability in 94% of trials. The comparison with traditional methods showed superiority in precision, efficiency and accessibility, confirming technical and economic viability for small companies. The modularity of the design allows future expansions with IoT, strengthening local competitiveness and preserving the quality of perishable products.

Abstract—This paper describes the redesign of an intelligent firefighter helmet prototype aimed at enhancing safety during wildfire suppression in Honduras. Building upon a base system that monitors vital signs (heart rate and blood oxygen), ambient temperature, posture, and location via LoRa technology, the enhanced design integrates several key improvements: a high-speed calibration GPS module, carbon monoxide (CO) detection, and ruggedized thermal and waterproof housing for the electronic components. Furthermore, a redesigned monitoring interface presents both quantitative and qualitative data to provide comprehensive situational awareness for the user and command center. Experimental results demonstrate a GPS accuracy of ±2.5 m, reliable hazardous gas detection, and structural integrity at temperatures up to 85°C. This system improves occupational safety and emergency management by providing real-time data for informed strategic decision-making in wildfire scenarios.