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8 June 2024 Automated rapid identification of feed materials in total mixed rations for dairy cattle
Vincent Nwaneri, Daniel Uyeh, Patience Mba, Daniel Morris
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Proceedings Volume 13034, Real-Time Image Processing and Deep Learning 2024; 130340G (2024) https://doi.org/10.1117/12.3013810
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States
Abstract
In the global agricultural landscape, dairy cattle are of paramount economic importance because they produce essential products like milk, butter, and cheese. Ensuring their well-being and sustaining production necessitate effective feed management. Traditional methods for assessing feed quality are labor-intensive and destructive, posing risks of resource wastage and production interruptions. This study addresses this challenge by introducing a novel approach to classify feed materials and Total Mixed Rations (TMR) for dairy cattle. Utilizing RGB images and a dual-branch neural network based on the VGG16 architecture, the model achieved 86.72% accuracy in feed categorization. This automates real-time feed analysis, offering high precision, and lays the foundation for further advancements in precision animal production through deep learning in practical agricultural contexts.
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Vincent Nwaneri, Daniel Uyeh, Patience Mba, and Daniel Morris "Automated rapid identification of feed materials in total mixed rations for dairy cattle", Proc. SPIE 13034, Real-Time Image Processing and Deep Learning 2024, 130340G (8 June 2024); https://doi.org/10.1117/12.3013810
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KEYWORDS
Agriculture
Animals
Neural networks
RGB color model
Automation
Databases
Deep learning
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