Semi Supervised Machine Learning for Livestock Threat Classification Using GPS Data

South African livestock farmers face major challenges in the form of livestock theft and predation. In response to these concerns, farmers started using a collar that monitors the acceleration of an animal and, when specific parameters are met, triggers an alarm which transmits GPS data to the user’s mobile application. Typically, a collar is placed on one animal per flock or herd. In this work, we aim to classify the GPS trajectories captured by these devices into four categories: theft, predation, own-handling and other. We lay particular emphasis on distinguishing theft alarms since these have direct implications for the safety and financial sustainability of farmers. To date, just over one million of these alarms have been recorded. Unfortunately, these trajectories are not labelled with the four categories. Therefore, we start by collecting labelled data sets that can be used for training classification models. We then investigate supervised and semi-supervised approaches for classifying the trajectories. Our semi-supervised approach shows the best results with performance comparable to human performance. The approach consists of three parts. First, an autoencoder and classifier are jointly trained to produce fixed-dimensional embeddings from GPS trajectories. Second, these embeddings are clustered to produce cluster labels. And lastly, the cluster labels are added to human-engineered features and used to train a final classifier. Our semi-supervised approach achieves an overall classification accuracy of 69%, with an F1 score of 56% for theft events (4% lower than human performance) and an F1 score of 90% for own-handling events (slightly outperforming a human). This model can be deployed to aid farmers in terms of safety and security by providing them with critical information in emergency situations.