Color-Aware Two-Branch DCNN for Efficient Plant Disease Classification

Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.

Keywords

CNN, DCNN, Deep Learning, Plant Disease, CIE LAB, Neural Networks, Artificial Intelligence, Multipath

Citation

Mendel. 2022 vol. 28, č. 2, s. 55-62. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/176

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0