https://revistas.uepg.br/index.php/ijac <p>The Iberoamerican Journal of Applied Computing is an academic and scientific journal, with an interdisciplinary character and with submission and publication of articles in continuous flow. The purpose of the journal is to disseminate the scientific production inherent to Applied Computing in the various areas of knowledge, valuing original research and reflections.</p> <p>ISSN 2237-4523</p> Universidade Estadual de Ponta Grossa pt-BR Iberoamerican Journal of Applied Computing 2237-4523 https://revistas.uepg.br/index.php/ijac/article/view/23429 <p>The identification of plant species is essential in botany and has attracted the interest of researchers in the field of computer science. Such identification requires the assistance of botany experts and, due to the substantial number of species and the similarity among them, it can be time-consuming and subjective. To automate the process of plant identification, computer systems that capture and process plant images have been considered. These systems use machine learning and therefore require image samples for training and model construction. Among the techniques that can be used for machine learning, convolutional neural networks have shown promise due to their ability to use images without prior preprocessing and background information. This work investigates the use of machine learning through convolutional neural networks to identifying plant species. For this, a new dataset of images from 35 plant species were created, collecting images from an arboreal collection, and, using data augmentation, this dataset was expanded. This dataset was used to evaluate the accuracies of four convolutional neural network models. The better accuracy value was equal to 89%, when using the MobileNetV2 model.</p> Isabelly dos Santos Pacheco Rosimeri de Oliveira Fragoso Lilian Tais de Gouveia Luciano Jose Senger Copyright (c) 2024 Iberoamerican Journal of Applied Computing 2024-05-29 2024-05-29 12 1