1.华中农业大学信息学院/农业农村部智慧养殖技术重点实验室/农业智能技术教育部工程研究中心/ 湖北省农业大数据工程技术研究中心，武汉430070;2.武汉软件工程职业学院信息学院，武汉430205;3.华中农业大学深圳营养与健康研究院/中国农业科学院深圳农业基因组研究所/ 岭南现代农业科学与技术广东省实验室深圳分中心，深圳518000
姚超，E-mail：chaoyao@webmail.hzau.edu.cn
李国亮，E-mail：guoliang.li@mail.hzau.edu.cn
S8-01;S24
中央高校基本科研业务费专项（2662022JC005）；华中农业大学-中国农业科学院深圳农业基因组研究所合作基金（SZYJY2022001）
- YAO Chao 1,2
YAO Chao
College of Informatics， Huazhong Agricultural University/Key Laboratory of Smart Farming Technology for Agricultural Animals， Ministry of Agriculture and Rural Affairs/Engineering Research Center of Intelligent Technology for Agriculture， Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data，Wuhan 430070，China;College of Information， Wuhan Vocational College of Software and Engineering，Wuhan 430070，China
在期刊界中查找
在百度中查找
在本站中查找 - NI Fuchuan 1
NI Fuchuan
College of Informatics， Huazhong Agricultural University/Key Laboratory of Smart Farming Technology for Agricultural Animals， Ministry of Agriculture and Rural Affairs/Engineering Research Center of Intelligent Technology for Agriculture， Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data，Wuhan 430070，China
在期刊界中查找
在百度中查找
在本站中查找 - LI Guoliang 1,3
LI Guoliang
College of Informatics， Huazhong Agricultural University/Key Laboratory of Smart Farming Technology for Agricultural Animals， Ministry of Agriculture and Rural Affairs/Engineering Research Center of Intelligent Technology for Agriculture， Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data，Wuhan 430070，China;Shenzhen Institute of Nutrition and Health，Huazhong Agricultural University， Shenzhen Branch， Guangdong Laboratory for Lingnan Modern Agriculture，Genome Analysis Laboratory of the Ministry of Agriculture，Agricultural Genomics Institute at Shenzhen， Chinese Academy of Agricultural Sciences，Shenzhen 518000，China
在期刊界中查找
在百度中查找
在本站中查找
1.College of Informatics， Huazhong Agricultural University/Key Laboratory of Smart Farming Technology for Agricultural Animals， Ministry of Agriculture and Rural Affairs/Engineering Research Center of Intelligent Technology for Agriculture， Ministry of Education/Hubei Engineering Technology Research Center of Agricultural Big Data，Wuhan 430070，China;2.College of Information， Wuhan Vocational College of Software and Engineering，Wuhan 430070，China;3.Shenzhen Institute of Nutrition and Health，Huazhong Agricultural University， Shenzhen Branch， Guangdong Laboratory for Lingnan Modern Agriculture，Genome Analysis Laboratory of the Ministry of Agriculture，Agricultural Genomics Institute at Shenzhen， Chinese Academy of Agricultural Sciences，Shenzhen 518000，China
- 摘要 |
- 图/表 |
- 访问统计 |
- 参考文献 |
- 相似文献 |
- 引证文献 | |
- 文章评论
图像分割作为智慧农业养殖中“视觉系统”的重要组成部分，被广泛应用于畜禽的智慧养殖中。近年来，深度学习算法飞速发展，基于深度学习的图像分割技术也取得了重大突破。这些方法赋予了分割区域更准确的语义信息，使得图像分割更加精准和智能，为畜禽智慧养殖提供了更强的技术支持。本文通过广泛收集和整理国内外研究的相关文献，重点阐述了图像分割技术在畜禽养殖中的畜禽计数、体尺体质量测量、姿态估计与行为识别、体况及疾病检测、精准饲养等方面的应用现状，给出了如何根据实际性能需求（精度、处理速度）、数据集、计算资源等方面选择合适图像分割方法的建议，总结分析了当前研究中与畜禽养殖相关且可用于图像分割训练的公开数据集；并指出了基于深度学习的图像分割技术在畜禽养殖中所面临的挑战与未来的发展趋势，希望能为畜禽养殖中图像分割技术的具体应用提供参考。
Image segmentation， as an important component of the vision system in smart agricultural farming， is widely used in the smart farming of livestock and poultry. In recent years， deep learning algorithms have been booming， and image segmentation technology based on deep learning have also made significant breakthroughs. These methods give more accurate semantic information to the segmented region， making the segmentation more accurate and intelligent， and providing stronger technical support for poultry and livestock smart farming. Through extensive collection and analysis of relevant domestic and foreign research literature， this paper first elaborates the application of image segmentation based on deep learning of poultry and livestock farming in detail， such as measurement of body size and weight， attitude estimation and behavior recognition， body condition and disease detection， precision feeding， etc. Suggestions are given on how to choose appropriate image segmentation methods based on actual performance requirements （accuracy， processing speed）， datasets， and computational resources. Furthermore， the open datasets， which are summarized and analyzed in current literature， related to livestock and poultry farming can be used for image segmentation training. This paper points out the challenges and future development trends of image segmentation technology based on deep learning in livestock and poultry farming， hoping to provide reference for the specific application of image segmentation technology in livestock and poultry farming.
引用本文
姚超,倪福川,李国亮.基于深度学习的图像分割在畜禽养殖中的应用研究进展[J].华中农业大学学报,2023,42(3):39-46
复制
文章指标
- 点击次数:
- 下载次数:
- HTML阅读次数:
历史
- 收稿日期:2022-10-10
- 在线发布日期: 2023-06-20
文章二维码
