<![CDATA[The development of precision livestock farming requires robust and automated data collection tools to minimize animal stress and improve farm efficiency. Traditional livestock weighing methods often lack immediate data access and do not support dynamic resource planning. This study designs and implements a Smart Squeeze Cage based on the Internet of Things (IoT) integrated with a Random Forest Regressor algorithm for real-time livestock weight monitoring and feed optimization. The system integrates four load cell sensors connected in parallel, an HX711 amplifier, and an ESP32 microcontroller embedded within a customized Squeeze Cage structure. Weight data is transmitted via wireless protocol to a centralized cloud database using Supabase and PostgreSQL. Based on historical data, the Random Forest model automatically predicts livestock weight trends and calculates daily feed requirements to provide intelligent recommendations. Testing results indicate high sensor precision with an accuracy of 97% (error tolerance of 0,5 kg), data transmission latency of 1.2 seconds, and a successful data delivery rate of 99.1%. This system offers a seamless, non-invasive solution for data-driven livestock management in modern farming environments.]]>
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