Beef Cattle Production and Management Practices and Implications for Educators

Vestal, Mallory K.; Ward, Clement E.; Doye, Damona; Lalman, David

doi:10.22004/ag.econ.21426

Cited by 2 publications

(1 citation statement)

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“…Therefore, large-scale farming to improve the productivity and production level of the meat and dairy industry is an effective way to increase farmers’ income, ensure food safety, improve the ability to prevent and control epidemics, and achieve the coordinated development of animal husbandry and the environment. Sheep identification is the basis of intelligent farm management, and currently common techniques for individual identification, such as hot iron tagging [ 1 ], freeze tagging [ 2 ], and ear notching [ 3 ], are available. These methods are prone to cause serious physical damage to animals [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face

Song

Liu

Wang

et al. 2022

Animals

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Accurate identification of sheep is important for achieving precise animal management and welfare farming in large farms. In this study, a sheep face detection method based on YOLOv3 model pruning is proposed, abbreviated as YOLOv3-P in the text. The method is used to identify sheep in pastures, reduce stress and achieve welfare farming. Specifically, in this study, we chose to collect Sunit sheep face images from a certain pasture in Xilin Gol League Sunit Right Banner, Inner Mongolia, and used YOLOv3, YOLOv4, Faster R-CNN, SSD and other classical target recognition algorithms to train and compare the recognition results, respectively. Ultimately, the choice was made to optimize YOLOv3. The mAP was increased from 95.3% to 96.4% by clustering the anchor frames in YOLOv3 using the sheep face dataset. The mAP of the compressed model was also increased from 96.4% to 97.2%. The model size was also reduced to 1/4 times the size of the original model. In addition, we restructured the original dataset and performed a 10-fold cross-validation experiment with a value of 96.84% for mAP. The results show that clustering the anchor boxes and compressing the model using this dataset is an effective method for identifying sheep. The method is characterized by low memory requirement, high-recognition accuracy and fast recognition speed, which can accurately identify sheep and has important applications in precision animal management and welfare farming.

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Section: Introductionmentioning

confidence: 99%