Mobile App Development for Monitoring Goat Activities

Abstract

Samy M.S. Elmasri, Noor Idayu Mohd Tahir and Ammar A.M. Al-Talib

"Mobile App Development for Monitoring Goat Activities" aims to create an automated and efficient system for managing goat feeding and water consumption. The primary objectives are to build a prototype using a camera and sensors for data collection, analyze a data logging system to record feeding habits and water consumption, and develop a mobile application to allow farmers to monitor the feeding system in real-time. The prototype, constructed using a Raspberry Pi 4 equipped with a camera module and ultrasonic sensors, collects real-time data on the status of feeding troughs, detecting whether they are empty, partially empty, or full. A YOLOv8n deep learning model was trained on a comprehensive dataset of images to classify these statuses accurately. The data logging system records and analyzes feeding habits and water consumption patterns, providing valuable insights into the goats' behavior and ensuring they have consistent access to food and water. The mobile application developed for this project, integrated with the Blynk app, provides farmers with real-time notifications and detailed reports on the feeding system's status. This allows for timely interventions, better resource management, and improved animal welfare. The project successfully combines hardware and software solutions to enhance the efficiency of goat farming, reducing manual labor and setting a foundation for future innovations in automated livestock management. By demonstrating the potential of integrating AI and IoT technologies in agriculture, this project highlights the benefits of continuous monitoring and timely alerts in maintaining a well-managed and sustainable farming operation.

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