Open AccessResearch on Sheep Recognition Algorithm Based on Deep Learning in Animal Husbandry
Author(s) -
Chao Ma,
Xin Sun,
Changliang Yao,
Tian Mao,
Linyi Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012129
Subject(s) - animal husbandry , adaptability , artificial intelligence , computer science , machine learning , agriculture , biology , ecology
At this stage, the domestic animal husbandry industry has a very important support role in the domestic market economy, and the sheep industry has received extensive attention as one of the important industries. Limb movements and activities can directly reflect the adaptability of sheep to the breeding environment and conditions, and provide a richer scientific and technical experience for sheep breeding. The sheep target detection is an important prerequisite for grasping the movement behavior of sheep. Therefore, how to efficiently and accurately identify and detect sheep has become the key to the development of sheep industry at this stage. In this paper, the Faster-RCNN neural network model based on the Soft-NMS algorithm is studied, which realizes the real-time detection and positioning of sheep under complex breeding conditions, and improves the accuracy of recognition while ensuring the detection speed. Experiments show that the proposed detection model can detect sheep with 95.32% accuracy and mark the location of the target in real time, which provides an effective data foundation for sheep behavior research and helps promote the development of high-tech animal husbandry effect.