Individual Cattle Identification Using a Deep Learning Based Framework. Issue 30 (2019) - Qiao, Yongliang
Abstract: Individual cattle identification is required for precision livestock farming. Current methods for individual cattle identification requires either visual, or unique radio frequency, ear tags. We propose a deep learning based framework to identify beef cattle using image sequences unifying the advantages of both CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) network methods. A CNN network was used (Inception-V3) to extract features from a rear-view cattle video dataset and these extracted features were then used to train an LSTM model to capture temporal information and identify each individual animal. A total of 516 rear- view videos of 41 cattle at three time points separated by one month were collected. Our method achieved an accuracy of 88% and 91% for 15-frame and 20-frame video length, respectively. Our approach outperformed the framework that only uses CNN (identification accuracy 57%). Our framework will now be further improved using additional data before integrating the system into on-farm management processes.