To certify the information coming from the registered animals of different breeds and to guarantee their racial purity and contribute to the genetic improvement, we propose the development of a model based on augmented reality and support decision making for identification and automatic classification of Brown Swiss cattle. TensorFlow Object Detection API was used to detect the cow in real time. The learning transfer approach was used for training, and MobilNet pre-trained architecture was selected. MobilNet is an efficient model for mobile applications because it is small in size and fasts. The results were reflected in the development of a mobile app, which was evaluated through the automatic adjustment and calibration of the template on the cow if the animal that was focusing was or was not of the Brown Swiss breed. / Trabajo de investigación
| Identifer | oai:union.ndltd.org:PUCP/oai:tesis.pucp.edu.pe:20.500.12404/15431 |
| Date | 27 November 2019 |
| Creators | Apumayta Lopez, Julianna Milagros |
| Contributors | Alatrista Salas, Hugo, Nuñez del Prado, Miguel |
| Publisher | Pontificia Universidad Católica del Perú, PE |
| Source Sets | Pontificia Universidad Católica del Perú |
| Language | Spanish |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/closedAccess |
Page generated in 0.0017 seconds