A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification
Plant diseases pose a significant challenge for food production and safety. Therefore, it is indispensable to correctly identify plant diseases for timely intervention to protect crops from massive losses. The application of computer vision technology in phytopathology has increased exponentially du...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/2/228 |
| _version_ | 1797483804456124416 |
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| author | Anil Bhujel Na-Eun Kim Elanchezhian Arulmozhi Jayanta Kumar Basak Hyeon-Tae Kim |
| author_facet | Anil Bhujel Na-Eun Kim Elanchezhian Arulmozhi Jayanta Kumar Basak Hyeon-Tae Kim |
| author_sort | Anil Bhujel |
| collection | DOAJ |
| description | Plant diseases pose a significant challenge for food production and safety. Therefore, it is indispensable to correctly identify plant diseases for timely intervention to protect crops from massive losses. The application of computer vision technology in phytopathology has increased exponentially due to automatic and accurate disease detection capability. However, a deep convolutional neural network (CNN) requires high computational resources, limiting its portability. In this study, a lightweight convolutional neural network was designed by incorporating different attention modules to improve the performance of the models. The models were trained, validated, and tested using tomato leaf disease datasets split into an 8:1:1 ratio. The efficacy of the various attention modules in plant disease classification was compared in terms of the performance and computational complexity of the models. The performance of the models was evaluated using the standard classification accuracy metrics (precision, recall, and F1 score). The results showed that CNN with attention mechanism improved the interclass precision and recall, thus increasing the overall accuracy (>1.1%). Moreover, the lightweight model significantly reduced network parameters (~16 times) and complexity (~23 times) compared to the standard ResNet50 model. However, amongst the proposed lightweight models, the model with attention mechanism nominally increased the network complexity and parameters compared to the model without attention modules, thereby producing better detection accuracy. Although all the attention modules enhanced the performance of CNN, the convolutional block attention module (CBAM) was the best (average accuracy 99.69%), followed by the self-attention (SA) mechanism (average accuracy 99.34%). |
| first_indexed | 2024-03-09T22:53:16Z |
| format | Article |
| id | doaj.art-738d5a87128d4cd4afcfa7b46457f82b |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T22:53:16Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-738d5a87128d4cd4afcfa7b46457f82b2023-11-23T18:16:45ZengMDPI AGAgriculture2077-04722022-02-0112222810.3390/agriculture12020228A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease ClassificationAnil Bhujel0Na-Eun Kim1Elanchezhian Arulmozhi2Jayanta Kumar Basak3Hyeon-Tae Kim4Department of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, KoreaDepartment of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, KoreaDepartment of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, KoreaSmart Farm Research Center, Gyeongsang National University, Jinju 52828, KoreaDepartment of Bio-Systems Engineering, Gyeongsang National University, Jinju 52828, KoreaPlant diseases pose a significant challenge for food production and safety. Therefore, it is indispensable to correctly identify plant diseases for timely intervention to protect crops from massive losses. The application of computer vision technology in phytopathology has increased exponentially due to automatic and accurate disease detection capability. However, a deep convolutional neural network (CNN) requires high computational resources, limiting its portability. In this study, a lightweight convolutional neural network was designed by incorporating different attention modules to improve the performance of the models. The models were trained, validated, and tested using tomato leaf disease datasets split into an 8:1:1 ratio. The efficacy of the various attention modules in plant disease classification was compared in terms of the performance and computational complexity of the models. The performance of the models was evaluated using the standard classification accuracy metrics (precision, recall, and F1 score). The results showed that CNN with attention mechanism improved the interclass precision and recall, thus increasing the overall accuracy (>1.1%). Moreover, the lightweight model significantly reduced network parameters (~16 times) and complexity (~23 times) compared to the standard ResNet50 model. However, amongst the proposed lightweight models, the model with attention mechanism nominally increased the network complexity and parameters compared to the model without attention modules, thereby producing better detection accuracy. Although all the attention modules enhanced the performance of CNN, the convolutional block attention module (CBAM) was the best (average accuracy 99.69%), followed by the self-attention (SA) mechanism (average accuracy 99.34%).https://www.mdpi.com/2077-0472/12/2/228attention moduleconvolutional neural networkslightweight networktomato diseasedisease detection |
| spellingShingle | Anil Bhujel Na-Eun Kim Elanchezhian Arulmozhi Jayanta Kumar Basak Hyeon-Tae Kim A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification Agriculture attention module convolutional neural networks lightweight network tomato disease disease detection |
| title | A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification |
| title_full | A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification |
| title_fullStr | A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification |
| title_full_unstemmed | A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification |
| title_short | A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification |
| title_sort | lightweight attention based convolutional neural networks for tomato leaf disease classification |
| topic | attention module convolutional neural networks lightweight network tomato disease disease detection |
| url | https://www.mdpi.com/2077-0472/12/2/228 |
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