Image analysis for individual identification and feeding behaviour monitoring of dairy cows based on Convolutional Neural Networks (CNN)

Abstract

In precision livestock farming, individual identification and analysis of feeding behaviour have a great impact on optimising the productivity and improving health monitoring. The sensors usually used to measure several parameters from an individual dairy cow (RFID, Accelerometer, etc.) are invasive, uncomfortable and stressful for animals. To overcome these limits, we have developed a non-invasive system based entirely on image analysis. The top of the dairy cow's head image, captured in a dairy cow farm, was used as a Region of Interest (ROI) and different classifiers based on Convolutional Neural Network (CNN) model are used to monitor the feeding behaviour and perform individual identification of seventeen Holstein dairy cows. We use one CNN to detect the dairy cow presence in the feeder zone. A second CNN is used to determine the dairy cow position in front of the feeder, standing or feeding. A third CNN is used to check the availability of food in the feeder and if so recognise the food category. The last CNN is devoted to individual identification of the dairy cow. Furthermore, we also explore the contribution of a CNN coupled to Support Vector Machine (SVM) and the combination of multiple CNNs in the individual identification process. For the evaluation step, we have used a dataset composed of 7265 images of 17 Holstein dairy cows during feeding periods on a commercial farm. Results show that our method yields high scores in each step of our algorithm.