Plant Diseases Recognition from Digital Images using Multichannel Convolutional Neural Networks

Andre da Silva Abade, Ana Paula G. S. de Almeida, Flavio de Barros Vidal

2019

Abstract

Plant diseases are considered one of the main factors influencing food production and to minimize losses in production, it is essential that crop diseases have a fast detection and recognition. Nowadays, recent studies use deep learning techniques to diagnose plant diseases in an attempt to solve the main problem: a fast, low-cost and efficient methodology to diagnose plant diseases. In this work, we propose the use of classical convolutional neural network (CNN) models trained from scratch and a Multichannel CNN (M-CNN) approach to train and evaluate the PlantVillage dataset, containing several plant diseases and more than 54,000 images (divided into 38 diseases classes with 14 plant species). In both proposed approaches, our results achieved better accuracies than the state-of-the-art, with faster convergence and without the use of transfer learning techniques. Our multichannel approach also demonstrates that the three versions of the dataset (colored, grayscaled and segmented) can contribute to improve accuracy, adding relevant information to the proposed artificial neural network.

Paper Citation

in Harvard Style

Abade A., S. de Almeida A. and Vidal F. (2019). Plant Diseases Recognition from Digital Images using Multichannel Convolutional Neural Networks. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 450-458. DOI: 10.5220/0007383904500458

in Bibtex Style

@conference{visapp19,
author={Andre da Silva Abade and Ana Paula G. S. de Almeida and Flavio de Barros Vidal},
title={Plant Diseases Recognition from Digital Images using Multichannel Convolutional Neural Networks},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={450-458},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007383904500458},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Plant Diseases Recognition from Digital Images using Multichannel Convolutional Neural Networks
SN - 978-989-758-354-4
AU - Abade A.
AU - S. de Almeida A.
AU - Vidal F.
PY - 2019
SP - 450
EP - 458
DO - 10.5220/0007383904500458
PB - SciTePress