<i>ViT-SmartAgri</i>: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture
Invading pests and diseases always degrade the quality and quantity of plants. Early and accurate identification of plant diseases is critical for plant health and growth. This work proposes a smartphone-based solution using a Vision Transformer (ViT) model for identifying healthy plants and unhealt...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/14/2/327 |
| _version_ | 1797299194793295872 |
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| author | Utpal Barman Parismita Sarma Mirzanur Rahman Vaskar Deka Swati Lahkar Vaishali Sharma Manob Jyoti Saikia |
| author_facet | Utpal Barman Parismita Sarma Mirzanur Rahman Vaskar Deka Swati Lahkar Vaishali Sharma Manob Jyoti Saikia |
| author_sort | Utpal Barman |
| collection | DOAJ |
| description | Invading pests and diseases always degrade the quality and quantity of plants. Early and accurate identification of plant diseases is critical for plant health and growth. This work proposes a smartphone-based solution using a Vision Transformer (ViT) model for identifying healthy plants and unhealthy plants with diseases. The collected dataset of tomato leaves was used to collectively train Vision Transformer and Inception V3-based deep learning (DL) models to differentiate healthy and diseased plants. These models detected 10 different tomato disease classes from the dataset containing 10,010 images. The performance of the two DL models was compared. This work also presents a smartphone-based application (Android App) using a ViT-based model, which works on the basis of the self-attention mechanism and yielded a better performance (90.99% testing) than Inception V3 in our experimentation. The proposed <i>ViT-SmartAgri</i> is promising and can be implemented on a colossal scale for smart agriculture, thus inspiring future work in this area. |
| first_indexed | 2024-03-07T22:46:10Z |
| format | Article |
| id | doaj.art-f79f75e415af4fcc94b3e46e148da26e |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-07T22:46:10Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-f79f75e415af4fcc94b3e46e148da26e2024-02-23T15:04:11ZengMDPI AGAgronomy2073-43952024-02-0114232710.3390/agronomy14020327<i>ViT-SmartAgri</i>: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart AgricultureUtpal Barman0Parismita Sarma1Mirzanur Rahman2Vaskar Deka3Swati Lahkar4Vaishali Sharma5Manob Jyoti Saikia6Faculty of Computer Technology, Assam down town University, Guwahati 781026, IndiaDepartment of Information Technology, Gauhati University, Guwahati 781014, IndiaDepartment of Information Technology, Gauhati University, Guwahati 781014, IndiaDepartment of Information Technology, Gauhati University, Guwahati 781014, IndiaDepartment of Information Technology, Gauhati University, Guwahati 781014, IndiaDepartment of Information Technology, Gauhati University, Guwahati 781014, IndiaDepartment of Electrical Engineering, University of North Florida, Jacksonville, FL 32224, USAInvading pests and diseases always degrade the quality and quantity of plants. Early and accurate identification of plant diseases is critical for plant health and growth. This work proposes a smartphone-based solution using a Vision Transformer (ViT) model for identifying healthy plants and unhealthy plants with diseases. The collected dataset of tomato leaves was used to collectively train Vision Transformer and Inception V3-based deep learning (DL) models to differentiate healthy and diseased plants. These models detected 10 different tomato disease classes from the dataset containing 10,010 images. The performance of the two DL models was compared. This work also presents a smartphone-based application (Android App) using a ViT-based model, which works on the basis of the self-attention mechanism and yielded a better performance (90.99% testing) than Inception V3 in our experimentation. The proposed <i>ViT-SmartAgri</i> is promising and can be implemented on a colossal scale for smart agriculture, thus inspiring future work in this area.https://www.mdpi.com/2073-4395/14/2/327vision transformertomato diseasesViTInception V3android appsmart agriculture |
| spellingShingle | Utpal Barman Parismita Sarma Mirzanur Rahman Vaskar Deka Swati Lahkar Vaishali Sharma Manob Jyoti Saikia <i>ViT-SmartAgri</i>: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture Agronomy vision transformer tomato diseases ViT Inception V3 android app smart agriculture |
| title | <i>ViT-SmartAgri</i>: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture |
| title_full | <i>ViT-SmartAgri</i>: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture |
| title_fullStr | <i>ViT-SmartAgri</i>: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture |
| title_full_unstemmed | <i>ViT-SmartAgri</i>: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture |
| title_short | <i>ViT-SmartAgri</i>: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture |
| title_sort | i vit smartagri i vision transformer and smartphone based plant disease detection for smart agriculture |
| topic | vision transformer tomato diseases ViT Inception V3 android app smart agriculture |
| url | https://www.mdpi.com/2073-4395/14/2/327 |
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