Counting Cattle in Argentina with AI and Satellite Images

Abstract

From a specific need of the national institution responsible for plant and animal health in Argentina, SENASA, this collaborative work between the public sectors together with academia arises. The objective was the development of a Deep Learning algorithm for the detection of livestock, cows in particular, in very high-resolution satellite images (provided by the Argentine Space Agency (CONAE)) and its subsequent counting. The basic elements involved in the development of artificial intelligence are detailed, such as the selection and acquisition of satellite images, their very thorough preprocessing and labelling, details of the training stage and some forms of error quantification. The image database is made up of about 320 scenes (based on very high resolution (VHR) satellite data from the Pleiades and Pleiades NEO sensors, ©Airbus 2022, distributed by CONAE) of the Pampas and Patagonian regions in Argentina. Around 8,000 labels of different types of animals were generated, the most common were cows and sheep. Also labels that did not represent animals to contribute to training. Finally, some very promising preliminary results are presented, such as the average error in the count that was achieved was ± four cows. The usefulness of these tools is reflected in better management of renewable natural resources linked to animal health issues, fiscal issues and within the framework of the 2030 Agenda and Sustainable Development Goals #12, #15 and #17.