Enhancing Broiler Weight Prediction via Preprocessed Kernel Density Estimation

Accurate broiler weight estimation in commercial farms is hindered by noisy scale data and multi-broiler occupancy. To address this challenge, we propose a KDE-based framework enhanced with systematic preprocessing, including coefficient of variation (CV), relative change (ROC), and absolute change (AC). In this study, kernel density estimation (KDE) is employed not as a predictive model, but as a distributional tool to robustly extract representative flock weight from noisy, high-frequency scale measurements under commercial farm conditions. In the absence of physical ground-truth, our evaluation focused on the framework’s ability to consistently detect the single, representative peak in the KDE distribution. Weekly thresholds were empirically optimized for the preprocessing filters. Results show that the combined ROC + AC method consistently produced unimodal peak distributions and improved the Peak Detection Rate (PDR) from 91.2% (raw data) to 97.9%. Single-Entity Filtering, assisted by cameras, further mitigated density distortions caused by prolonged occupancy, while CV-only and ROC-only filtering yielded less stable representative values. These findings demonstrate that rigorous preprocessing is essential for reliable KDE-based weight estimation under real-world farm conditions. The proposed framework not only improves data quality and stabilizes distributions but also provides a practical foundation for real-time monitoring and AI-driven precision livestock farming models.