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Cherry = ['[Powdery_mildew]', '[healthy]']
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Corn = ['[Cercospora_leaf_spot] [Gray_leaf_spot]', '[Common_rust]', '[Northern_Leaf_Blight]', '[healthy]']
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Grape = ['[Black_rot]', '[Esca_Black_Measles]', '[Leaf_blight]', '[healthy]']
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Peach = ['[Bacterial_spot]', '[healthy]']
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Pepper = ['[Bacterial_spot]', '[healthy]']
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Potato = ['[Early_blight]', '[Late_blight]', '[healthy]']
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Tomato = ['[Target_Spot]', '[YellowLeaf_Curl_Virus]', '[Bacterial_spot]', '[Early_blight]', '[healthy]', '[Late_blight]', '[Leaf_Mold]', '[Septoria_leaf_spot]', '[Spider_mites]']
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```
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We train a general model for all types of fruit, which means this model can be used to examine all 8 fruit and corresponding conditions. 8 specialized models for a single fruit are also trained for better recognition accuracy.
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<img src="demonstration/kinds.png" width="60%">
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## Software Environment
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Project Framework:Flask + Bootstrap
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Development Environment:Pycharm
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Database:MySql
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ORM library: SQLAlchemy
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## Requirements
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```
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$ git clone https://github.com/lujiazho/DetectWeb.git
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$ cd DetectWeb
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$ pip install -r requirements.txt
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```
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## Demonstration
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|Interface Ⅰ|Appearance|Interface Ⅱ|Appearance
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|---|---|---|---
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|<p align="center">Detection</p>|<img src="demonstration/detection.png" width="100%">|<p align="center">Main Interface</p>|<img src="demonstration/main.png" width="100%" alt="" align=center />
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|<p align="center">Record</p>|<img src="demonstration/record.png" width="100%">|<p align="center">Trend</p>|<img src="demonstration/trend.png" width="100%" alt="" align=center />
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title: Css
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emoji: 🏢
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colorFrom: indigo
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colorTo: red
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sdk: docker
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pinned: false
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license: apache-2.0
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