Purpose-The livestock industry is undergoing a critical transition to intensive,large-scale farming.Intelligent monitoring technologies are essential for improving epidemic early warning systems,reducing breeding cost...Purpose-The livestock industry is undergoing a critical transition to intensive,large-scale farming.Intelligent monitoring technologies are essential for improving epidemic early warning systems,reducing breeding costs,and promoting sustainable production.This study aimed to develop a novel pig behavior recognition method using advanced computer vision technology to support intelligent livestock farming.Design/methodology/approach-The YOLOv5 model was utilized to achieve contactless and efficient monitoring of daily pig activities.The study enhanced the YOLOv5 model by improving its input mechanism,backbone network and by incorporating the shuffle attention module.These modifications significantly improved the ability of the model to capture and interpret the spatiotemporal features of pig behavior.Findings-The experimental results demonstrate that compared with the original YOLOv5 model,the Precision,Recall,mAP@0.5 and mAp@0.5:0.95 of the proposed model has improved by 3.0%,2.3%,2.6%and 10.5%,respectively.These findings showcase the model’s effectiveness and potential for real-world applications in intelligent livestock farming,and highlight the feasibility of employing advanced computer vision models to enhance monitoring and management in animal farming environments.Originality/value-This study presents a novel approach to pig behavior recognition by integrating cutting-edge computer vision techniques with YOLOv5 enhancements.This study contributes to the field by addressing the challenges of spatiotemporal feature extraction and demonstrating the practical application of these methods in intelligent livestock farming.Future research directions include generalization to other animal species,integration with other sensor data,teal-time monitoring and decision support and application in wildlife and laboratory animal research,thus further advancing the intelligent breeding industry.展开更多
基金supported by the Natural Science Foundation of Fujian Province(2022J011178)the young and middle-aged teachers education research project of Fujian Province(JAT210422)the Sanming College Scientific Research Development Fund(B202103).
文摘Purpose-The livestock industry is undergoing a critical transition to intensive,large-scale farming.Intelligent monitoring technologies are essential for improving epidemic early warning systems,reducing breeding costs,and promoting sustainable production.This study aimed to develop a novel pig behavior recognition method using advanced computer vision technology to support intelligent livestock farming.Design/methodology/approach-The YOLOv5 model was utilized to achieve contactless and efficient monitoring of daily pig activities.The study enhanced the YOLOv5 model by improving its input mechanism,backbone network and by incorporating the shuffle attention module.These modifications significantly improved the ability of the model to capture and interpret the spatiotemporal features of pig behavior.Findings-The experimental results demonstrate that compared with the original YOLOv5 model,the Precision,Recall,mAP@0.5 and mAp@0.5:0.95 of the proposed model has improved by 3.0%,2.3%,2.6%and 10.5%,respectively.These findings showcase the model’s effectiveness and potential for real-world applications in intelligent livestock farming,and highlight the feasibility of employing advanced computer vision models to enhance monitoring and management in animal farming environments.Originality/value-This study presents a novel approach to pig behavior recognition by integrating cutting-edge computer vision techniques with YOLOv5 enhancements.This study contributes to the field by addressing the challenges of spatiotemporal feature extraction and demonstrating the practical application of these methods in intelligent livestock farming.Future research directions include generalization to other animal species,integration with other sensor data,teal-time monitoring and decision support and application in wildlife and laboratory animal research,thus further advancing the intelligent breeding industry.
