Detection, identification and posture recognition of cattle with satellites, aerial photography and UAVs using deep learning techniques

To obtain specific information about cattle in extensive production systems, the usual labor intensive work done by the farmer to find and visit cattle herds in large pastures can be replaced by using UAVs. UAVs are capable of assessing traits in cows, like distinguishing individuals and postures. Although these traits and the detection of cattle, do not represent resilience and efficiency directly, these may contain information associated to resilience. We performed a feasibility study of remotely sensed imagery (using datasets from satellites, manned aircrafts, and UAVs), and deep learning techniques to detect, count, identify and characterize posture of individual cows in grassland production systems. With these techniques, we focused on : (1) automatic detection of cattle locations and animal counting; (2) cow postures like standing, grazing or lying; and (3) individual cow identification. Data were collected during three field trials in the Netherlands and Poland. Artificial Intelligence was used to classify the objects (cattle) in the drone imagery. Classification accuracies of >95% were obtained for detecting cows. Accuracies of similar to 91% were obtained for identifying individual cows, and accuracies of similar to 88% were obtained for cow postures. These results make camera-mounted drones a promising new technology for monitoring extensive beef production systems.