Diseño y desarrollo de aplicación móvil para la clasificación de flora nativa chilena utilizando redes neuronales convolucionales

06/11/2021
by   Ignacio Muñoz, et al.
0

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 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.

READ FULL TEXT

page 6

page 10

page 11

page 13

page 14

page 15

research
07/12/2022

LudVision – Remote Detection of Exotic Invasive Aquatic Floral Species using Drone-Mounted Multispectral Data

Remote sensing is the process of detecting and monitoring the physical c...
research
03/18/2018

The Automatic Identification of Butterfly Species

The available butterfly data sets comprise a few limited species, and th...
research
10/12/2018

AppIntent: Intuitive Automation Specification Framework for Mobile AppTesting

The proliferation of mobile apps and reduced time in mobile app releases...
research
03/05/2021

NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil

Phytoparasitic nematodes (or phytonematodes) are causing severe damage t...
research
07/30/2019

Marine Mammal Species Classification using Convolutional Neural Networks and a Novel Acoustic Representation

Research into automated systems for detecting and classifying marine mam...
research
11/26/2018

Cross-domain Deep Feature Combination for Bird Species Classification with Audio-visual Data

In recent decade, many state-of-the-art algorithms on image classificati...
research
08/07/2017

Identifying 3 moss species by deep learning, using the "chopped picture" method

In general, object identification tends not to work well on ambiguous, a...

Please sign up or login with your details

Forgot password? Click here to reset