The well-being of pets is essential for owners. This project developed an automatic pet feeder that leverages Internet of Things technology and deep learning to address feeding challenges. The feeder integrates sensors, including a weight sensor for portion control, a camera for pet identification, an ultrasonic sensor for proximity detection, and a servo motor for dispensing food. A microcontroller for real-time monitoring and processing controls these components. Based on YOLOv5 and trained on a dataset of dog images, the DL model ensures accurate pet recognition and customized feeding. Results show that the system effectively identifies pets and dispenses appropriate portions based on weight, ensuring precise and personalized feeding. The sensor data fusion provides reliable information about pet characteristics. Overall, the smart feeder offers a convenient and efficient solution for managing pet nutrition, improving pet health, and increasing owner convenience.

This study develops a statistical and technological framework to analyze extreme rainfall in Colima, Mexico, by integrating historical precipitation records, probabilistic modeling, and spatial visualization. Using data from CONAGUA meteorological stations, we identify high-intensity rainfall events and model their recurrence using the Generalized Extreme Value (GEV) distribution to estimate key return periods. The results support flood-risk assessment and territorial planning in Colima. Spatial interpolation was performed in Python (version 3.13), and QGIS (version 3.38) produces exceedance maps that illustrate geographic variations in rainfall intensity across the state. These exceedance maps reveal a consistent spatial pattern, with the northern and western areas of Colima experiencing the highest frequencies of extreme events. Based on these results, the integration of real-time sensor technologies and satellite observations may improve flood monitoring and risk management frameworks.