Accurate re-identification of individual cows is crucial for effective herd management in precision cattle farming. However, this task is challenging in real-world scenarios due to variability in cow appearances and environmental conditions as well as the limited number of reference images available for re-identification. This paper addresses the problem of cow re-identification under open-set and few-shot conditions, where the system must recognize previously unseen individuals with limited annotated data. Metric learning was used to train a neural network for re-identification and its performance was evaluated using K-nearest neighbors (KNN). The neural network is applied to two datasets: OpenSetCows2020 and MultiCamCows2024 captured on different farms. Four testing variants are proposed that resemble different real-life situations: initial deployment, barn change, addition of new cows and cross-farm generalization. The proposed framework directly addresses the challenges in real-world cattle farming, and allows for a more in-depth analysis of the characteristics and applicability of re-identification methods from a practical perspective than existing evaluation metrics.
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Bošnjak, Andrej; Džijan, Matej; Nyarko, Emmanuel K.; Cupec, Robert
How Many Images Are Required to Recognize a Cow? //
Appl. Sci., 15 (2025), 17, 9809 doi:10.3390/app15179809
https://doi.org/10.3390/app15179809
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