In recent years, the state of Chihuahua has experienced overexploitation of natural resources, habitat fragmentation, contamination of soils and bodies of water, and desertification in several areas, in addition to the modification of the environment for urban expansion, which has led to the loss of biodiversity in the state. In view of this situation, there is a need for technological tools to evaluate and monitor the presence and absence of species in specific regions. Some of the most relevant organisms for this analysis are the birds of the state of Chihuahua, since they are excellent bioindicators of the state of the ecosystems and their presence is related to a high level of biodiversity. To address these situations of biodiversity loss, convolutional neural networks (CNN) are very useful, since these methodologies have demonstrated great effectiveness in different areas involving object recognition, due to their ability to learn automatically from the patterns, shapes and colors present in the images. In the context of population ecology, the application of CNNs allows the analysis of many images at the same time, in a fast way, which can contribute and facilitate the identification and monitoring of species, as well as the changes that occur in their populations, facilitating decision-making for conservation and sustainable development.