A Binary Neural Network with Dual Attention for Plant Disease Classification
Plant disease control has long been a critical issue in agricultural production and relies heavily on the identification of plant diseases, but traditional disease identification requires extensive experience. Most of the existing deep learning-based plant disease classification methods run on high-...
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| Format: | Article |
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MDPI AG
2023-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/21/4431 |
| _version_ | 1797631978311254016 |
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| author | Ping Ma Junan Zhu Gan Zhang |
| author_facet | Ping Ma Junan Zhu Gan Zhang |
| author_sort | Ping Ma |
| collection | DOAJ |
| description | Plant disease control has long been a critical issue in agricultural production and relies heavily on the identification of plant diseases, but traditional disease identification requires extensive experience. Most of the existing deep learning-based plant disease classification methods run on high-performance devices to meet the requirements for classification accuracy. However, agricultural applications have strict cost control and cannot be widely promoted. This paper presents a novel method for plant disease classification using a binary neural network with dual attention (DABNN), which can save computational resources and accelerate by using binary neural networks, and introduces a dual-attention mechanism to improve the accuracy of classification. To evaluate the effectiveness of our proposed approach, we conduct experiments on the PlantVillage dataset, which includes a range of diseases. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mn>1</mn><mspace width="0.222222em"></mspace><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>c</mi><mi>c</mi><mi>u</mi><mi>r</mi><mi>a</mi><mi>c</mi><mi>y</mi></mrow></semantics></math></inline-formula> of our method reach 99.39% and 99.4%, respectively. Meanwhile, compared to AlexNet and VGG16, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>o</mi><mi>m</mi><mi>p</mi><mi>u</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>i</mi><mi>o</mi><mi>n</mi><mi>a</mi><mi>l</mi><mspace width="0.222222em"></mspace><mi>c</mi><mi>o</mi><mi>m</mi><mi>p</mi><mi>l</mi><mi>e</mi><mi>x</mi><mi>i</mi><mi>t</mi><mi>y</mi></mrow></semantics></math></inline-formula> of our method is reduced by 72.3% and 98.7%, respectively. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>a</mi><mi>r</mi><mi>a</mi><mi>m</mi><mi>s</mi><mspace width="0.222222em"></mspace><mi>s</mi><mi>i</mi><mi>z</mi><mi>e</mi></mrow></semantics></math></inline-formula> of our algorithm is 5.4% of AlexNet and 2.3% of VGG16. The experimental results show that DABNN can identify various diseases effectively and accurately. |
| first_indexed | 2024-03-11T11:31:21Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T11:31:21Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-14a7582e22c94644a906b5023414d6b72023-11-10T15:01:24ZengMDPI AGElectronics2079-92922023-10-011221443110.3390/electronics12214431A Binary Neural Network with Dual Attention for Plant Disease ClassificationPing Ma0Junan Zhu1Gan Zhang2School of Internet, Anhui University, Hefei 230039, ChinaSchool of Internet, Anhui University, Hefei 230039, ChinaSchool of Internet, Anhui University, Hefei 230039, ChinaPlant disease control has long been a critical issue in agricultural production and relies heavily on the identification of plant diseases, but traditional disease identification requires extensive experience. Most of the existing deep learning-based plant disease classification methods run on high-performance devices to meet the requirements for classification accuracy. However, agricultural applications have strict cost control and cannot be widely promoted. This paper presents a novel method for plant disease classification using a binary neural network with dual attention (DABNN), which can save computational resources and accelerate by using binary neural networks, and introduces a dual-attention mechanism to improve the accuracy of classification. To evaluate the effectiveness of our proposed approach, we conduct experiments on the PlantVillage dataset, which includes a range of diseases. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mn>1</mn><mspace width="0.222222em"></mspace><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>c</mi><mi>c</mi><mi>u</mi><mi>r</mi><mi>a</mi><mi>c</mi><mi>y</mi></mrow></semantics></math></inline-formula> of our method reach 99.39% and 99.4%, respectively. Meanwhile, compared to AlexNet and VGG16, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>o</mi><mi>m</mi><mi>p</mi><mi>u</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>i</mi><mi>o</mi><mi>n</mi><mi>a</mi><mi>l</mi><mspace width="0.222222em"></mspace><mi>c</mi><mi>o</mi><mi>m</mi><mi>p</mi><mi>l</mi><mi>e</mi><mi>x</mi><mi>i</mi><mi>t</mi><mi>y</mi></mrow></semantics></math></inline-formula> of our method is reduced by 72.3% and 98.7%, respectively. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>a</mi><mi>r</mi><mi>a</mi><mi>m</mi><mi>s</mi><mspace width="0.222222em"></mspace><mi>s</mi><mi>i</mi><mi>z</mi><mi>e</mi></mrow></semantics></math></inline-formula> of our algorithm is 5.4% of AlexNet and 2.3% of VGG16. The experimental results show that DABNN can identify various diseases effectively and accurately.https://www.mdpi.com/2079-9292/12/21/4431plant diseaseleaf imagebinary neural networkdual attention |
| spellingShingle | Ping Ma Junan Zhu Gan Zhang A Binary Neural Network with Dual Attention for Plant Disease Classification Electronics plant disease leaf image binary neural network dual attention |
| title | A Binary Neural Network with Dual Attention for Plant Disease Classification |
| title_full | A Binary Neural Network with Dual Attention for Plant Disease Classification |
| title_fullStr | A Binary Neural Network with Dual Attention for Plant Disease Classification |
| title_full_unstemmed | A Binary Neural Network with Dual Attention for Plant Disease Classification |
| title_short | A Binary Neural Network with Dual Attention for Plant Disease Classification |
| title_sort | binary neural network with dual attention for plant disease classification |
| topic | plant disease leaf image binary neural network dual attention |
| url | https://www.mdpi.com/2079-9292/12/21/4431 |
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