Dise\~no y desarrollo de aplicaci\'on m\'ovil para la clasificaci\'on de flora nativa chilena utilizando redes neuronales convolucionales

• URL: http://arxiv.org/abs/2106.06592v1
• Date: Fri, 11 Jun 2021 19:43:47 GMT
• Title: Dise\~no y desarrollo de aplicaci\'on m\'ovil para la clasificaci\'on de flora nativa chilena utilizando redes neuronales convolucionales
• Authors: Ignacio Mu\~noz, Alfredo Bolt
• Abstract summary: This study introduces the development of a chilean species dataset and an optimized classification model implemented to a mobile app. The data set was built by putting together pictures of several species captured on the field and by selecting some pictures available from other datasets available online. The best models were implemented on a mobile app, obtaining a 95% correct prediction rate with respect to the set of tests.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Introduction: Mobile apps, through artificial vision, are capable of recognizing vegetable species in real time. However, the existing species recognition apps do not take in consideration the wide variety of endemic and native (Chilean) species, which leads to wrong species predictions. This study introduces the development of a chilean species dataset and an optimized classification model implemented to a mobile app. Method: the data set was built by putting together pictures of several species captured on the field and by selecting some pictures available from other datasets available online. Convolutional neural networks were used in order to develop the images prediction models. The networks were trained by performing a sensitivity analysis, validating with k-fold cross validation and performing tests with different hyper-parameters, optimizers, convolutional layers, and learning rates in order to identify and choose the best models and then put them together in one classification model. Results: The final data set was compounded by 46 species, including native species, endemic and exotic from Chile, with 6120 training pictures and 655 testing pictures. The best models were implemented on a mobile app, obtaining a 95% correct prediction rate with respect to the set of tests. Conclusion: The app developed in this study is capable of classifying species with a high level of accuracy, depending on the state of the art of the artificial vision and it can also show relevant information related to the classified species.

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