With the increasing global demand for chicken production and welfare, the intelligent and efficient gathering of phenotypic data using non-contact detection procedures will be important for contemporary poultry breeding. This research delineates the pivotal function of non-contact detection technologies in the precise gathering of poultry phenotypic data, emphasizing their application in non-contact monitoring techniques, including sensors and cameras, to improve on-farm chicken observation without disruption. In-depth examination of multifaceted advancements includes the application of convolutional neural networks for monitoring chicken appearance, facilitating recognition of feather coverage and crown color in intensive farming settings. Progress in efficient and precise breeding is outlined, encompassing body size measurements, weight calculation, and external appearance identification in a non-contact environment. Innovations in poultry growth monitoring and relevant case studies are showcased, illustrating how research findings can enhance production and animal welfare. Today, although there are challenges such as complex environments and high equipment costs, combined with future innovative technologies, it is expected to solve these difficulties and improve the efficiency, welfare and sustainability level of poultry farming.