Version 1
: Received: 16 August 2023 / Approved: 17 August 2023 / Online: 18 August 2023 (08:28:28 CEST)
How to cite:
Singh, V.; Rees, M.; Hampton, S.; Annadurai, S. Plant Identification Using Convolution Neural Network and Vision Transformer-Based Models. Preprints2023, 2023081330. https://doi.org/10.20944/preprints202308.1330.v1
Singh, V.; Rees, M.; Hampton, S.; Annadurai, S. Plant Identification Using Convolution Neural Network and Vision Transformer-Based Models. Preprints 2023, 2023081330. https://doi.org/10.20944/preprints202308.1330.v1
Singh, V.; Rees, M.; Hampton, S.; Annadurai, S. Plant Identification Using Convolution Neural Network and Vision Transformer-Based Models. Preprints2023, 2023081330. https://doi.org/10.20944/preprints202308.1330.v1
APA Style
Singh, V., Rees, M., Hampton, S., & Annadurai, S. (2023). Plant Identification Using Convolution Neural Network and Vision Transformer-Based Models. Preprints. https://doi.org/10.20944/preprints202308.1330.v1
Chicago/Turabian Style
Singh, V., Simon Hampton and Sivaram Annadurai. 2023 "Plant Identification Using Convolution Neural Network and Vision Transformer-Based Models" Preprints. https://doi.org/10.20944/preprints202308.1330.v1
Abstract
Identification of plants is a challenging task which aims to identify the family, genus, and species level according to morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users to narrow down the possibilities. However, numerous morphological similarities between and within species make the classification difficult. In this paper, we tested a custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88K and 16K images for classifying plants at genus and species levels respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. The ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. ViT models also perform better for classifying plants at the species level compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
