Enhanced Prediction of Broiler Shipment Weight Using Vision-Assisted Load Cell Analysis

Accurate prediction of broiler shipment weight is essential for optimizing production planning and meeting market demand. Previous studies have estimated representative daily weight values from load cell data using K-means clustering and kernel density estimation (KDE) and have applied forecasting models such as Prophet, ARIMA, and Gompertz. Among these, the combination of K-means and Prophet demonstrated the best performance. In this study, we propose an enhanced method integrating computer vision with load cell measurements. The YOLOv8n model localizes broilers in images, while a 5-pixel edge region, both inside and outside the weighing platform boundaries, filters invalid weight values. This enables accurate broiler counting on the weighing platform. The instantaneous population mean weight distribution is estimated by dividing the total measured weight by the detected broiler count. The representative daily weight values are then calculated through averaging. Additionally, we compare five outlier processing methods to evaluate their effectiveness in improving prediction accuracy. Experimental results show that our method achieves a prediction error of less than 50 g for broiler shipment weights, which will significantly improve farm operation efficiency and reduce feeding cost losses. This approach has already been deployed in selected farms and is ready for comprehensive implementation.