Review on image-based animals weight weighing

Large-scale animal husbandry currently serves as the primary source of protein and essential nutrients for human consumption. Ensuring adequate access to animal weight has emerged as a pivotal approach for monitoring animal health and production performance. However, with a plethora of weighing methods available, the need to compare these diverse technologies according to the specific requirements of various animal species has become a pressing concern. Addressing this issue is imperative for the advancement of weighing technology. Consequently, we have undertaken a comprehensive literature review that delves into the developmental trajectory of weighing technology and provides in-depth descriptions of its corresponding characteristics. We aim to discern the technical challenges encountered at different stages of development and offer a thorough insight into the strategies employed to overcome these challenges. In our investigation, we have amalgamated evaluation criteria from various weighing methods into this developmental lineage. Our primary works are as follows: First, we compiled a comprehensive collection of animal weight application scenarios, encompassing species such as pigs, cattle, ducks, broilers, and fish. We systematically organized weight measurement methods proposed by researchers tailored to the unique requirements of each scenario. Secondly, we delved into the evolution of animal weighing techniques, considering the inherent limitations of direct weighing methods. We meticulously cataloged feature parameter extraction methods from various studies within the traditional image-based weighing domain and conducted an in-depth comparative analysis of not only the weighing methodologies themselves but also their associated evaluation metrics. Third, our focus shifted to addressing the deficiencies of traditional image-based weighing techniques. We honed in on deep learning-based approaches, systematically dissecting the critical components of image acquisition, preprocessing, model training, and evaluation. In this examination, we scrutinized the vulnerabilities and potential remedies associated with image quality's impact on the precision of animal weight estimation. Lastly, in light of the current state of research, we explored the challenges that persist in the realm of deep learning-based animal weighing. Within this context, we put forth prospective solutions to overcome these obstacles. We hope this review will provide a clearer understanding of the evolution of weighing methods and explore potential solutions to the challenges faced in deep learning weighing methods currently. This will, in turn, foster the advancement of weighing systems for various applications.