Uso de Redes Neuronales Convolucionales para el Reconocimiento Automático de Imágenes de Macroinvertebrados para el Biomonitoreo Participativo


In Panama, there are community organizations that guarantee access to water for human consumption to more than 20% of the country's total population. For the sustainability of the water resource, it is essential to involve the communities in the process of monitoring the water quality. This can be achieved through the implementation of participatory biomonitoring using macroinvertebrates as indicators. In fact, it has been determined that the presence of different families of these organisms in ecosystems can be associated to different levels of their ecological quality. This work aims to develop a system capable of recognizing two families of macroinvertebrates through the use of images. The system is based on the use of algorithms of deep neural networks, with which we can achieve the learning of patterns. From a set of public images from the internet and biomonitoring carried out in the field, we train a convolutional neural network implemented in Tensorflow and Keras. These images belong to photographs of specimens of the families Calopterygidae and Heptageniidae. For this preliminary test, we report reliability percentages with values above 95%.

Keywords: image recognition, neural networks, convolutional neural networks, macroinvertebrates

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