Performance of Various Deep-Learning Networks in the Seed Classification Problem

We report the results of an in-depth study of 15 variants of five different Convolutional Neural Network (CNN) architectures for the classification of seeds of seven different grass species that possess symmetry properties. The performance metrics of the nets are investigated in relation to the comp...

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Bibliographic Details
Published in:Symmetry (Basel) 2021-10, Vol.13 (10), p.1892
Main Authors: Eryigit, Recep, Tugrul, Bulent
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Datasets
densenet
efficientnet
Grasses
Machine learning
mobilenet
Neural networks
Parameters
Performance measurement
resnet
seed classification
Seeds
VGG
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Summary:We report the results of an in-depth study of 15 variants of five different Convolutional Neural Network (CNN) architectures for the classification of seeds of seven different grass species that possess symmetry properties. The performance metrics of the nets are investigated in relation to the computational load and the number of parameters. The results indicate that the relation between the accuracy performance and operation count or number of parameters is linear in the same family of nets but that there is no relation between the two when comparing different CNN architectures. Using default pre-trained weights of the CNNs was found to increase the classification accuracy by ≈3% compared with training from scratch. The best performing CNN was found to be DenseNet201 with a 99.42% test accuracy for the highest resolution image set.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym13101892