Deep learning with aerial surveys for extensive livestock hotspot recognition in the Brazilian Semi-arid Region

Abstract

In the Brazilian Semi-arid Region, extensive livestock farming with ecoproductive management is the most efficient way to maintain and increase the production of goat products (e.g., meat) with of not depleting environmental resources. This set of actions (induced goat migration and pasture closure) is part of Livestock 4.0, in which Industry 4.0 feed areas are efficiently managed using artificial intelligence and deep learning properly monitored by the producer and the consumer. The objective of this work was to identify pasture areas with Opuntia ficus-indica (Mill, Cactaceae) forage palm species for breeding and production of Capra aegagrus-hircus goats (Lineu, Bovidae) using aerial survey images captured by drones classified using deep learning techniques. The methodological steps of the Industry Architecture Reference Model 4.0 were adapted to the field situation (Semi-arid Region) including (A) study area delimitation, (B) image collection (by drones), (C) deep learning training, convolutional neural network (CNN) training, (D) training accuracy analysis, and (E) automatic goat production evaluation and validation. The area classification based on the forage palm density allowed us to measure the environmental degradation caused by livestock. Stimulated goat migration reduced this degradation as well as increased goat biomass and volume production.