Deep learning for Chilean native flora classification: a comparative analysis

Carola Figueroa-Flores; Pablo San-Martin

doi:10.3389/fpls.2023.1211490

Deep learning for Chilean native flora classification: a comparative analysis

Front Plant Sci. 2023 Sep 11:14:1211490. doi: 10.3389/fpls.2023.1211490. eCollection 2023.

Authors

Carola Figueroa-Flores¹, Pablo San-Martin²

Affiliations

¹ Department of Computer Science and Information Technology, Universidad del Bío Bío, Chillán, Chile.
² School of Computer and Information Engineering, Universidad del Bío-Bío, Chillán, Chile.

Abstract

The limited availability of information on Chilean native flora has resulted in a lack of knowledge among the general public, and the classification of these plants poses challenges without extensive expertise. This study evaluates the performance of several Deep Learning (DL) models, namely InceptionV3, VGG19, ResNet152, and MobileNetV2, in classifying images representing Chilean native flora. The models are pre-trained on Imagenet. A dataset containing 500 images for each of the 10 classes of native flowers in Chile was curated, resulting in a total of 5000 images. The DL models were applied to this dataset, and their performance was compared based on accuracy and other relevant metrics. The findings highlight the potential of DL models to accurately classify images of Chilean native flora. The results contribute to enhancing the understanding of these plant species and fostering awareness among the general public. Further improvements and applications of DL in ecology and biodiversity research are discussed.

Keywords: Chilean native flora; convolutional neural network; deep learning; image classification; transfer learning.