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Identification of species of plants of the Solanum (Solanaceae) genus native to Mexico using computational vision and convolutional neural networks on pictures of herbarium specimens
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-02) Hernández Rincón, Carlos Eduardo; Falcón Morales, Luis Eduardo; emipsanchez; Rodríguez Contreras, Aarón; Mendoza Montoya, Omar; Escuela de Ingeniería y Ciencias EIC; Campus Guadalajara
The development of Deep Learning techniques like Convolutional Neural Networks for automated image processing has been making big strides in recent years. This has helped to find more practical applications in many science fields. One such field is that of botanic taxonomic analysis which aims to accurately identify and classify new species of plants. It is important not only for scientific purposes but also for taking appropriate conservation actions, for economic reasons and for proper environment policy making. However, doing this requires a lot of technical skills and time and the number of qualified people at herbaria and scientific institutions in Mexico is not enough. Moreover, a significant number of new plant species have already been collected but are sitting unidentified in herbaria across the country. The Solanum genus encompasses species such as potatoes, eggplants and tomatoes. It is one of the most diverse and important for its economic, nutritious and cultural value worldwide. Mexico is no exception, and it is home to many species both discovered and undiscovered. Currently there is a project at Universidad de Guadalajara to identify all species of the Solanum genus native to Mexico that have already been collected at different herbaria. Convolutional Neural Networks could help with this huge task. The main purpose of this research is to prove that a system to assist a human taxonomist identify these plants is feasible and indeed helpful.

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