# Development and Validation of Internet of Things-Enabled Weighing System for Cage-Free Poultry Houses

## TL;DR

This paper introduces an IoT-based weighing system for cage-free poultry that enables accurate and continuous weight monitoring to improve bird welfare.

## Contribution

The novel contribution is an IoT-enabled automated weighing platform with real-time processing and validation for cage-free poultry weight monitoring.

## Key findings

- The system showed a mean absolute percentage error below 3% and most measurements within 95% agreement limits.
- Improved accuracy in the second validation session suggests platform stability impacts performance.
- The system demonstrates potential for low-stress continuous weight monitoring in poultry farms.

## Abstract

Accurate body-weight monitoring is essential for assessing welfare in cage-free poultry. However, commercial farms continue to rely on manual weighing because of concerns regarding the accuracy and reliability of automated methods. This study developed and evaluated an Internet of things (IoT)-enabled weighing platform integrating load cells, an microcontroller, a Raspberry Pi 5, and Node-RED for data acquisition, processing, and visualization. The system recorded weight measurements at 1 Hz, detected individual weighing sessions, and applied a rolling-median filter to produce stable weight estimates. Validation was performed against a reference scale during two weighing sessions one week apart using 75 cage-free hens randomly selected from a flock of 750 Hy-Line W80 birds. Bland–Altman analysis and a linear mixed-effects model indicated a small overestimation of approximately 6–9 g, with most measurements falling within the 95% limits of agreement, while overall mean absolute percentage error remained below 3%. Improved accuracy during the second session suggests that platform stability influenced performance. Overall, the system demonstrates strong potential for continuous low-stress weight monitoring in poultry farms. Future improvements should focus on refining calibration methods, enhancing mechanical stability, and integrating bird identification and presence-detection mechanisms to further support flock management and welfare monitoring.