Transformative Technology in Poultry Management: 3D CNNs for Broiler Chicken Weight Prediction

In global food production, broiler chickens hold significant importance, which are traditionally managed through manual labour for both meat and egg production. However, the poultry farming industry has increasingly turned to automation to address challenges associated with manual labour and enhance efficiency. This research introduces a novel methodology for estimating chicken weight utilizing top-view videos as input and leveraging 3D Convolutional Neural Networks (CNNs). Accurate weight assessment is a critical aspect of poultry farming, influencing feeding strategies and resource allocation. Traditional manual weighing methods are laborious and time-intensive, necessitating the exploration of automated alternatives. The methodology involves dataset collection, preprocessing, and employing 3D CNNs for effective feature extraction. 3D CNN architecture, adept at capturing spatial and temporal features, consists of convolutional and pooling layers, followed by flatten and dense layers for weight prediction. The model achieves a remarkable accuracy of 95%, as evidenced in the experimental results. Experimental validation showcases the potential of this method as a non-invasive and efficient tool for estimating chicken weight. It contributes to the growing field of automated livestock management in agriculture.