AI-ML Powered Pig Behavior Classification and Body Weight Prediction

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dc.contributor.authorBharadwaj, Sanjana Manjunathen
dc.contributor.committeechairHa, Sook Shinen
dc.contributor.committeememberJones, Creed Farrisen
dc.contributor.committeememberHa, Dong S.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2024-06-01T08:03:10Zen
dc.date.available2024-06-01T08:03:10Zen
dc.date.issued2024-05-31en
dc.description.abstractPrecision livestock farming technologies have been widely researched over the last decade. These technologies help in monitoring animal health and welfare parameters in a continuous, automated fashion. Under this umbrella of precision livestock farming, this study focuses on activity classification and body weight prediction in pigs. Activity monitoring is essential for understanding the health and growth of pigs. To automate this task effectively, we propose efficient and accurate sensor-based deep learning (DL) solutions. Among these, the 2D Residual Networks emerged as the best performing model, achieving an accuracy of 95.6%. This accuracy was 15.6% higher than that of other machine learning approaches. Additionally, accurate pig weight estimation is crucial for pork production, as it provides valuable insights into growth rates, disease prevalence, and overall health. Traditional manual methods of estimating pig weights are time-consuming and labor-intensive. To address this issue, we propose a novel approach that utilizes deep learning techniques on depth images for weight prediction. Through a custom image preprocessing pipeline, we train DL models to extract meaningful information from depth images for weight prediction. Our findings show that XceptionNet gives promising results, with a mean absolute error of 2.82 kg and a mean absolute percentage error of 7.42%. In comparison, the best performing statistical model, support vector machine, achieved a mean absolute error of 4.51 kg mean absolute percentage error of 15.56%.en
dc.description.abstractgeneralWith the increasing demand for food production in recent decades, the livestock farming industry faces significant pressure to modernize its methods. Traditional manual tasks such as activity monitoring and body weight measurement have been time-consuming and labor-intensive. Moreover, manual handling of animals can cause stress, negatively affecting their health. To address these challenges, this study proposes deep learning-based solutions for both activity classification and automated body weight prediction. For activity classification, our solution incorporates strategic data preprocessing techniques. Among various learning techniques, our deep learning model, the 2D Residual Networks, achieved an accuracy of 95.6%, surpassing other approaches by 15.6%. Furthermore, this study also compares statistical models with deep learning models for the body weight prediction task. Our analysis demonstrates that deep learning models outperform statistical models in terms of accuracy and inference time. Specifically, XceptionNet yielded promising results, with a mean absolute error of 2.82 kg and a mean absolute percentage error of 7.42%, outperforming the best statistical model by nearly 8%.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:40872en
dc.identifier.urihttps://hdl.handle.net/10919/119217en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectImage Processingen
dc.subjectMachine Learningen
dc.subjectDeep Learningen
dc.subjectPrecision Livestock Farmingen
dc.titleAI-ML Powered Pig Behavior Classification and Body Weight Predictionen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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