A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/2/469 |
| _version_ | 1828190188138921984 |
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| author | Athanasios Anagnostis Gavriela Asiminari Elpiniki Papageorgiou Dionysis Bochtis |
| author_facet | Athanasios Anagnostis Gavriela Asiminari Elpiniki Papageorgiou Dionysis Bochtis |
| author_sort | Athanasios Anagnostis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T08:18:26Z |
| format | Article |
| id | doaj.art-114c148b1aa84d21b65305652a48916a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-12T08:18:26Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-114c148b1aa84d21b65305652a48916a2022-12-22T03:40:41ZengMDPI AGApplied Sciences2076-34172020-01-0110246910.3390/app10020469app10020469A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves IdentificationAthanasios Anagnostis0Gavriela Asiminari1Elpiniki Papageorgiou2Dionysis Bochtis3Institute for Bio-Economy and Agri-Technology (iBO), Center for Research and Technology—Hellas (CERTH), GR57001 Thessaloniki, GreeceInstitute for Bio-Economy and Agri-Technology (iBO), Center for Research and Technology—Hellas (CERTH), GR57001 Thessaloniki, GreeceInstitute for Bio-Economy and Agri-Technology (iBO), Center for Research and Technology—Hellas (CERTH), GR57001 Thessaloniki, GreeceInstitute for Bio-Economy and Agri-Technology (iBO), Center for Research and Technology—Hellas (CERTH), GR57001 Thessaloniki, GreeceAnthracnose 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.https://www.mdpi.com/2076-3417/10/2/469machine learningimage classificationfungal diseasesfast fourier transform |
| spellingShingle | Athanasios Anagnostis Gavriela Asiminari Elpiniki Papageorgiou Dionysis Bochtis A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification Applied Sciences machine learning image classification fungal diseases fast fourier transform |
| title | A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification |
| title_full | A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification |
| title_fullStr | A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification |
| title_full_unstemmed | A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification |
| title_short | A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification |
| title_sort | convolutional neural networks based method for anthracnose infected walnut tree leaves identification |
| topic | machine learning image classification fungal diseases fast fourier transform |
| url | https://www.mdpi.com/2076-3417/10/2/469 |
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