Preliminary study of image analysis and machine learning methods to detect cattle on pasture in images taken from DJI drones

Automation of tasks are progressing fast together with new improved technology. This together with increased availability of drones in various price-ranges has great potential for many automated computer vision tasks since drones are often equipped with high-tech cameras ofmany different kinds. The task of automated supervision is no more relevant than in the area of agriculture, where farmers are not only by law required to check on their livestock regularly but for their own benefit as well to carefully study their animal’s behaviour. This thesis explores the possibility of detecting cattle on different pastures using computer vision on images taken from drones by DJI. The project trains and evaluates two convolutional neural network models from Tensorflow object detection while discussing future research project of a potential live supervision system of cattle. This is done by comparing the two models and discussing external variables such as image acquisition distance to the cattle and its importance by using the two detection models as reference. This is developed further into arguments on how to acquire future data for a large-scale implementation of such a problem