Abstract
Personal record keeping, behaviour tracking, accurate feeding, disease prevention and control, and food traceability require the identification of dairy cows. This work proposes a unique identification that combines Mask R-CNN and ResNet101 to identify individual cows in milking parlours accurately. Using 265 Holstein cows in various positions, a facial image dataset of their faces was created using the milking hall’s webcam. The feature pyramid network-based Mask R-CNN instance segmentation model was trained to separate the cows’ faces from their backgrounds. The ResNet101 individual classification network was trained using the segmentation above data as input and cow individual numbers as output. Combining the two techniques led to the creation of the cow individual recognition model. According to experimental findings, the Mask R-CNN model has an average accuracy of 96.37% on the picture test set. The accuracy of the Resnet101-based individual classification network was 99.61% on the training set and 98.75% on the validation set, surpassing that of VGG16, GoogLeNet, ResNet34, and other networks. The study’s suggested individual recognition model outperformed the combined effect of the YOLO series model and ResNet101 in terms of test accuracy (97.58%). Furthermore, it outperforms the pairing of Mask R-CNN with VGG16, GoogleNet, and ResNet34. This research offers precision dairy farming an excellent technical basis for individual recognition.
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Li, Z. et al. (2023). Dairy Cow Individual Identification System Based on Deep Learning. In: Sun, F., Cangelosi, A., Zhang, J., Yu, Y., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2022. Communications in Computer and Information Science, vol 1787. Springer, Singapore. https://doi.org/10.1007/978-981-99-0617-8_15
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DOI: https://doi.org/10.1007/978-981-99-0617-8_15
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