Recent advances in pig behavior detection based on information perception technology

Abstract

Global demand for meat is increasing with the world’s population growth, which leads to the expansion of global pig breeding. For farming enterprises, the production efficiency is important. In the process of pig breeding, pig behavior will reflect some pieces of information such as health, welfare, and growth status, which indirectly impacts the production efficiency of farming enterprises. Therefore, the detection of daily pig behaviors is essential. With the development of sensing and artificial intelligence technologies, various information perception technologies have been used in pig behavior detection. This paper provides a comprehensive review of recent advances in information perception technology for pig behavior detection. The merits and demerits of different information perception technologies for pig target perception and behavior detection were analyzed first. Then different detection systems for pig behavior were compared. Subsequently, the public datasets for pig behavior were innovatively summarized. Based on these findings, this study identifies key challenges that persist in the application of information perception technologies for pig behavior detection. These challenges include the limited data dimensionality when using a single sensing modality, the difficulty of accurately perceiving individual behavioral information in group housing conditions, the uneven research focus across different types of behaviors, the limited variety and scale of publicly available pig behavior datasets, and the heavy reliance on manual data annotation. To address these issues, future research should integrate multiple sensing modalities to enrich data quality and dimensionality, develop target extraction and behavior detection models that balance accuracy with computational complexity, broaden the scope of studied behaviors to include those previously overlooked, construct more diverse and sufficiently large datasets, and adopt semi-supervised or unsupervised strategies for data annotation. This work will facilitate large-scale commercial applications of pig behavior detection and will lay a critical foundation for welfare-oriented pig farming.