深度学习在猪只饲养过程的应用研究进展

随着人工智能技术的兴起，深度学习技术发展迅猛，在图像及音频识别、自然语言处理、机器人技术、生物信息学、化学和金融等领域中应用广泛，也是目前发展精细畜牧业重要工具。养猪业是中国的重要农业产业，生猪的体况、行为及健康状况直接影响猪场的收入水平，通过深度学习技术可以快速、准确地了解猪只的相关信息并进行精确管理，提高猪只饲养效率及动物福利水平。该研究阐述了深度学习在目标猪只检测、猪只图像分割、猪只体况及异常监测、猪只行为识别上的应用现状，提出了深度学习技术在猪只饲养过程中的改进策略，以方便研究人员快速了解其研究进展。同时总结和分析了深度学习技术在猪只饲养过程中关于数据来源及数据集、应用范围、模型优化的不足与展望，提出应建立适用于猪只领域的公共数据集和统一的数据集标准，扩大深度学习的应用范围，优化DL（Deep Learning）模型以满足实际任务需求，将深度学习应用于猪场生产实践中。该研究旨为提高猪只福利化养殖和猪场经济效益提供依据，以推动深度学习在猪只饲养过程中应用的进一步发展。

Research progress of deep learning in the process of pig feeding

Abstract: China is the major producer and consumer of pork all around the world. In China, pork production has accounted for more than 60% of meat production for a long time. Nowadays, the pig industry has gradually shifted from decentralized and extensive breeding to intensive large-scale breeding, promoting production efficiency. At present, as people''s demand for the quantity and quality of pork is increasing, China''s pig industry faces the problem that the output and quality of pork can not meet people''s daily needs. With the rise of artificial intelligence technology in recent years, deep learning technology has been developed rapidly and widely used in image and audio recognition, natural language processing, robotics, bioinformatics, chemistry, finance, and other fields. It is also an essential tool in developing precision livestock farming. The body condition, behavior and health status of pigs could directly affect the income level of pig farms, so through using deep learning technology, we can quickly and accurately acquire the relevant information about pigs, and carry out precise management to improve the feeding efficiency and welfare levels of pigs. This paper expounds on the research progress and application status of deep learning technology used in target pig detection, pig image segmentation, pig body condition and abnormal monitoring, as well as pig behavior recognition. Then we put forward the improvement strategy of deep learning technology used in the process of pig feeding, which make it easier for researchers to understand. At the same time, we summarize and analyze the data sources and datasets, application scope, and model optimization of previous works using deep learning technology in pig breeding. In the field of data sources and datasets, mobile computer vision systems are currently more suitable than systems with many fixed cameras when they are applied in pig houses, therefore, further research could focus on how to use deep learning technology to mobile computer vision systems. Deep learning technology= requires a large amount of data to learn data features, which is a massive disadvantage for pig applications. To get pre-training weights suitable for agricultural data sets, a large number of public datasets and a unified dataset standard suited to the pig field should be established. In terms of application scope, due to the short application time of deep learning in the process of raising pigs, many critical occasions are not involved or seldom involved in existing research, so the application scope of deep learning should further expand. In model optimization, optimizing the model to meet the needs of practical application scenarios is the direction of future research. Optimizing the model to achieve a better balance between size and model performance requires further study. Optimizing the model to locate the start and end time of the behavior in unclipped video and recognize the behavior of target pigs is the challenge in identifying the behavior of pigs in future. China''s research on deep learning is at the top level, so the application prospect of deep learning in pig farming is highly expected. Combined with the actual production scene of pigs, deep learning technology will significantly contribute to the development of precision livestock farming.