Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism
Tomatoes are considered one of the most valuable vegetables around the world due to their usage and minimal harvesting period. However, effective harvesting still remains a major issue because tomatoes are easily susceptible to weather conditions and other types of attacks. Thus, numerous research s...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1467811/full |
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| author | Aarthi Chelladurai D.P. Manoj Kumar S. S. Askar Mohamed Abouhawwash Mohamed Abouhawwash |
| author_facet | Aarthi Chelladurai D.P. Manoj Kumar S. S. Askar Mohamed Abouhawwash Mohamed Abouhawwash |
| author_sort | Aarthi Chelladurai |
| collection | DOAJ |
| description | Tomatoes are considered one of the most valuable vegetables around the world due to their usage and minimal harvesting period. However, effective harvesting still remains a major issue because tomatoes are easily susceptible to weather conditions and other types of attacks. Thus, numerous research studies have been introduced based on deep learning models for the efficient classification of tomato leaf disease. However, the usage of a single architecture does not provide the best results due to the limited computational ability and classification complexity. Thus, this research used Transductive Long Short-Term Memory (T-LSTM) with an attention mechanism. The attention mechanism introduced in T-LSTM has the ability to focus on various parts of the image sequence. Transductive learning exploits the specific characteristics of the training instances to make accurate predictions. This can involve leveraging the relationships and patterns observed within the dataset. The T-LSTM is based on the transductive learning approach and the scaled dot product attention evaluates the weights of each step based on the hidden state and image patches which helps in effective classification. The data was gathered from the PlantVillage dataset and the pre-processing was conducted based on image resizing, color enhancement, and data augmentation. These outputs were then processed in the segmentation stage where the U-Net architecture was applied. After segmentation, VGG-16 architecture was used for feature extraction and the classification was done through the proposed T-LSTM with an attention mechanism. The experimental outcome shows that the proposed classifier achieved an accuracy of 99.98% which is comparably better than existing convolutional neural network models with transfer learning and IBSA-NET. |
| format | Article |
| id | doaj-art-84c2bbcdb6fa4bf099ea86242e320827 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-84c2bbcdb6fa4bf099ea86242e3208272025-01-21T08:37:00ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.14678111467811Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanismAarthi Chelladurai0D.P. Manoj Kumar1S. S. Askar2Mohamed Abouhawwash3Mohamed Abouhawwash4Department of Electronics and Communication Engineering, Sengunthar Engineering College, Tiruchengode, IndiaDepartment of Computer Science and Engineering, Kalpataru Institute of Technology, Tiptur, IndiaDepartment of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi ArabiaDepartment of Animal Science, Michigan State University, East Lansing, MI, United StatesDepartment of Mathematics, Faculty of Science, Mansoura University, Mansoura, EgyptTomatoes are considered one of the most valuable vegetables around the world due to their usage and minimal harvesting period. However, effective harvesting still remains a major issue because tomatoes are easily susceptible to weather conditions and other types of attacks. Thus, numerous research studies have been introduced based on deep learning models for the efficient classification of tomato leaf disease. However, the usage of a single architecture does not provide the best results due to the limited computational ability and classification complexity. Thus, this research used Transductive Long Short-Term Memory (T-LSTM) with an attention mechanism. The attention mechanism introduced in T-LSTM has the ability to focus on various parts of the image sequence. Transductive learning exploits the specific characteristics of the training instances to make accurate predictions. This can involve leveraging the relationships and patterns observed within the dataset. The T-LSTM is based on the transductive learning approach and the scaled dot product attention evaluates the weights of each step based on the hidden state and image patches which helps in effective classification. The data was gathered from the PlantVillage dataset and the pre-processing was conducted based on image resizing, color enhancement, and data augmentation. These outputs were then processed in the segmentation stage where the U-Net architecture was applied. After segmentation, VGG-16 architecture was used for feature extraction and the classification was done through the proposed T-LSTM with an attention mechanism. The experimental outcome shows that the proposed classifier achieved an accuracy of 99.98% which is comparably better than existing convolutional neural network models with transfer learning and IBSA-NET.https://www.frontiersin.org/articles/10.3389/fpls.2024.1467811/fullattention mechanismdata augmentationsegmentationtomato leaf diseaseTransductive Long Short-Term Memory |
| spellingShingle | Aarthi Chelladurai D.P. Manoj Kumar S. S. Askar Mohamed Abouhawwash Mohamed Abouhawwash Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism Frontiers in Plant Science attention mechanism data augmentation segmentation tomato leaf disease Transductive Long Short-Term Memory |
| title | Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism |
| title_full | Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism |
| title_fullStr | Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism |
| title_full_unstemmed | Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism |
| title_short | Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism |
| title_sort | classification of tomato leaf disease using transductive long short term memory with an attention mechanism |
| topic | attention mechanism data augmentation segmentation tomato leaf disease Transductive Long Short-Term Memory |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1467811/full |
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