This study aims to estimate the live weight of broilers using image processing and deep learning techniques. The proposed method is designed to optimize management processes in broiler production systems, reduce labor requirements and operational costs, minimize animal stress caused by direct human contact, and serve as an effective alternative to traditional manual weighing practices.
The study was conducted at a commercial broiler farm located in Kahramanmaraş, in the Mediterranean region of Türkiye. An automatically controlled measurement enclosure was constructed to capture broiler images with minimal human intervention. Digital cameras mounted at the top of the enclosure recorded images of broilers that spontaneously entered the enclosure.
By using these images, live weight estimation was carried out in two stages: the first stage involved morphological image processing in the MATLAB environment, while the second stage focused on deep learning-based modeling for prediction. During the image processing stage, multiple regression analysis was performed using the actual weights obtained through manual weighing and the estimated weights derived from image-based measurements.
The analysis resulted in an adjusted R² value of 0.97 and a standard error of ± 131 g (P