Method Applied To Animal Monitoring Through VANT Images
Abstract
One of the necessary demands in the cattle ranching of extensive management is the counting of animals in areas of tens of hectares, costly when carried out manually and locally. In this context, this work proposes and discusses the efficacy of a semi-autonomous, non-invasive method for remote
identification of animals in the field, applicable to precision livestock systems. The method was conceived from an exploratory research methodology based on remote sensing techniques that include image collection processes by aerial surveying with RGB camera embedded in unmanned aerial vehicle, persistence of
images obtained by means of storage in databases space-time and processing of stored images for the construction of a rural property ortomosaic succeeded by the application of patterns discovery processes, making use of machine learning, especially convolutional neural networks. According to the experiments
carried out, the method was effective, being able to identify and count annals from the collection of images made by UAV at 100 m height, with an accuracy of up to 95%, including the approximate geographical position of the animals to field.