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Open AccessArticle

A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification

1
Institute for Bio-Economy and Agri-Technology (iBO), Center for Research and Technology—Hellas (CERTH), GR57001 Thessaloniki, Greece
2
Department of Computer Science, University of Thessaly, GR35131 Lamia, Greece
3
Faculty of Technology, University of Thessaly, Geopolis Campus Ring Road of Larisa-Trikala, GR41500 Larisa, Greece
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 469; https://doi.org/10.3390/app10020469
Received: 7 December 2019 / Revised: 27 December 2019 / Accepted: 6 January 2020 / Published: 8 January 2020
(This article belongs to the Special Issue Applied Agri-Technologies)
Anthracnose is a fungal disease that infects a large number of trees worldwide, damages intensively the canopy, and spreads with ease to neighboring trees, resulting in the potential destruction of whole crops. Even though it can be treated relatively easily with good sanitation, proper pruning and copper spraying, the main issue is the early detection for the prevention of spreading. Machine learning algorithms can offer the tools for the on-site classification of healthy and affected leaves, as an initial step towards managing such diseases. The purpose of this study was to build a robust convolutional neural network (CNN) model that is able to classify images of leaves, depending on whether or not these are infected by anthracnose, and therefore determine whether a tree is infected. A set of images were used both in grayscale and RGB mode, a fast Fourier transform was implemented for feature extraction, and a CNN architecture was selected based on its performance. Finally, the best performing method was compared with state-of-the-art convolutional neural network architectures. View Full-Text
Keywords: machine learning; image classification; fungal diseases; fast Fourier transform machine learning; image classification; fungal diseases; fast Fourier transform
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Anagnostis, A.; Asiminari, G.; Papageorgiou, E.; Bochtis, D. A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification. Appl. Sci. 2020, 10, 469.

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