Desarrollo de un Sistema de Reconocimiento de Especies Vegetales para el Jardín Botánico de la UNL Basado en Inteligencia Artificial - Rivera Merchán, Hernán Alexander (2024)
Artificial intelligence has experienced an outstanding advance in recent years, finding applications in various areas to automate and optimize specific tasks. In the context of this project, the focus is on the application of artificial intelligence in the field of botany, specifically for the identification of plant species in the Reinaldo Espinosa Botanical Garden in the city of Loja. This work involves the development of three neural network algorithms, based on the predefined architectures MobileNet, VGG19 and ResNet18. In order to speed up the training process, a reduced database has been used, using approximately 12% of the original database. Different levels of accuracy for each of the algorithms were obtained: MobileNet achieved 94.83%, VGG19 62.25%, and ResNet18 60.42%. After evaluating the results, the MobileNet architecture was selected for its superior performance compared to the others, Subsequently, MobileNet was subjected to a complete training with the original database, which consists of 1354 images of 20 different plant species, achieving a final accuracy of 88.59%. As part of the practical implementation, an Android application was developed using the Flutter Framework, allowing the use of the algorithm for plant identification directly from mobile devices. Keywords: Neural Networks, VGG19, MobileNet, ResNet, Flutter, Plant Species Identification