Clasificación Automática para Animales en Peligro de Extinción de Colombia Usando Redes Neuronales Convolucionales

The extinction of different types of animals is a problem that has been growing over the years, and that, consequently, has caused environmental problems, such as climate change. Genetic diversity (biodiversity) is essential for the development of all species and human beings depend on it in their daily lives. When biodiversity decreases, human life expectancy is reduced, not only from an ecological point of view, but also from a resource point of view, even to be able to have species that are adapted to an ecological niche. This research will expose a computer strategy that over time has achieved great results; convolutional neural networks is a process that has facilitated the monitoring of different kinds of animals in recent years, this, in order to facilitate the process of recognition and counting of animals, focused on agriculture and zoology. For this, an architecture in the field of convolutional neural networks (CNN) will be used, Alexnet, which has references with very high results. In addition, the mathematical programming software Matlab is used for the development of the neural network. Getting of this way a result of accuracy of validation of 97,52%, with the use of a dataset with 3026 images, in where, 80% are used for training and 20% for validation.La extinción de distintos tipos de animales es un problema que ha ido creciendo a lo largo de los años y que, en consecuencia, ha provocado problemas medioambientales, como el cambio climático. La diversidad genética (biodiversidad) es esencial para el desarrollo de todas las especies y los seres humanos dependen de ella en su vida cotidiana. Cuando la biodiversidad disminuye, la esperanza de vida del ser humano se reduce, no sólo desde el punto de vista ecológico, sino también desde el punto de vista de los recursos, incluso para poder tener especies adaptadas a un nicho ecológico. En esta investigación se expondrá una estrategia informática que a lo largo del tiempo ha logrado grandes resultados; las redes neuronales convolucionales es un proceso que ha facilitado el monitoreo de diferentes tipos de animales en los últimos años, esto, con el fin de facilitar el proceso de reconocimiento y conteo de animales, enfocado a la agricultura y la zoología. Para ello, se utilizará una arquitectura en el campo de las redes neuronales convolucionales (CNN), Alexnet, que tiene referencias con resultados muy elevados. Además, se utiliza el software de programación matemática Matlab para el desarrollo de la red neuronal. Obteniendo de esta forma un resultado de precisión de validación del 97,52%, con la utilización de un conjunto de datos con 3026 imágenes, en donde, el 80% se utilizan para el entrenamiento y el 20% para la validación

The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area