## Plant Disease Detector
<br>
<img src="app/static/SS.png">
<br>
<p align="center">
<sub>
Created by
<a href="https://github.com/imskr">
<strong>Shubham Kumar </strong>
</a>
<strong>and</strong>
<a href="https://github.com/imskr/Plant_Disease_Detection/graphs/contributors">
<strong>other contributors</strong>
</a>
</sub>
</p>
<hr noshade>
<br>
## My Article in [TowardsDataScience](https://t.co/iVmRCeUiDI?amp=1)
Models are trained on the preprocessed dataset which can be downloaded [here](https://drive.google.com/open?id=0B_voCy5O5sXMTFByemhpZllYREU).
## Local Set-Up
### Local:
- It is recommended to set up the project inside a virtual environment to keep the dependencies separated.
* [Python](https://realpython.com/python-virtual-environments-a-primer/#why-the-need-for-virtual-environments)
* [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
- Activate your virtual environment.
- Install dependencies by running `pip install -r requirements.txt`.
- Start up the server by running `python app/server.py serve`.
- Visit <http://localhost:8080/> to explore and test.
### Docker:
*Make Sure the Docker is installed in your local Machine. [Click Here](https://docs.docker.com/install/) to know that how to install Docker*.
- **Mac:**
```bash
$ git clone https://github.com/imskr/Plant_Disease_Detection.git
$ cd Plant_Disease_Detection
$ docker build -t fastai-v3 .
$ docker run --rm -it -p 8080:8080 fastai-v3
```
**Go to http://localhost:8080/ to test your app.**
- **Windows:**
```PowerShell or Command Prompt
$ git clone https://github.com/imskr/Plant_Disease_Detection.git
$ cd Plant_Disease_Detection
$ docker build -t fastai-v3 .
$ docker run --rm -it -p 8080:8080 fastai-v3
```
**Go to http://localhost:8080/ to test your app.**
**Note:** Windows 10 Pro required.
- **Linux:**
```Terminal
$ git clone https://github.com/imskr/Plant_Disease_Detection.git
$ cd Plant_Disease_Detection
$ docker build -t fastai-v3 .
$ docker run --rm -it -p 8080:8080 fastai-v3
```
**Note:** If this doesn't work use `--no-cache` flag in the build command.
**Go to http://localhost:8080/ to test your app.**
## Deployment
- **Google Cloud Platform:**
The complete guideline to deploy the *Plant Disease Detection App* can be found [*here*](./deployment_guide/gcp_deployment.md)
- **AWS Elastic BeanStalk:**
The complete guideline to deploy the *Plant Disease Detection App* can be found [*here*](./deployment_guide/aws_deployment.md)
## Server Set-Up (For Training)
- **Google Cloud Platform (Intermediate)** - The complete tutorial can be found [*here*](https://course.fast.ai/start_gcp.html)
- **Gradient (Easy)** - The complete tutorial can be found [*here*](https://course.fast.ai/start_gradient.html)
- **AWS EC2 (Advance)** - The complete tutorial can be found [*here*](https://course.fast.ai/start_aws.html)
## Dataset Description:
|Name | No of Classes | Class Names
| ------------- |:-------------:|:-----------------:|
| Apple | 04 | 'Apple___Apple_scab','Apple___Black_rot','Apple___Cedar_apple_rust' 'Apple___healthy' |
| Blueberry | 01 | 'Blueberry___healthy' |
| Cherry | 02 | 'Cherry_(including_sour)_Powdery_mildew', 'Cherry_(including_sour)_healthy' |
| Corn | 04 | 'Corn___Cercospora_leaf_spot', 'Corn___Common_rust','Corn___Northern_Leaf_Blight','Corn___healthy' |
| Grape | 04 | 'Grape___Black_rot','Grape___Esca_(Black_Measles)','Leaf_blight_(Isariopsis_Leaf_Spot)','Grape___healthy' |
| Orange | 01 | 'Orange___Haunglongbing_(Citrus_greening)' |
| Peach | 02 | 'Peach___Bacterial_spot','Peach___healthy' |
| Pepper | 02 | 'Pepper,_bell___Bacterial_spot','Pepper,_bell___healthy' |
| Potato | 03 | 'Potato___Early_blight','Potato___Late_blight','Potato___healthy' |
| Raspberry | 01 | 'Raspberry___healthy' |
| Soyabean | 01 | 'Soybean___healthy' |
| Squash | 01 | 'Squash___Powdery_mildew' |
| Strawberry| 02 | 'Strawberry___Leaf_scorch','Strawberry___healthy' |
| Tomato | 10 | Tomato: 'Bacterial_spot','Early_blight', 'Late_blight', 'Leaf_Mold', 'Septoria_leaf_spot', 'Spider_mites','Target_Spot', 'Yellow_Leaf_Curl_Virus', 'Mosaic_virus', 'Healthy' |
Before making your valuable contribution to this project do check [CONTRIBUTING.md](https://github.com/imskr/Plant_Disease_Detection/blob/master/CONTRIBUTING.md) file.
## Citation
When using any part of this repo, please cite: [Plant Village Paper](https://arxiv.org/abs/1511.08060).
<br>
<p align='center'>
<a href="https://www.buymeacoffee.com/imskr" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: 41px !important;width: 174px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;" ></a>
</p>
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
数据收集: 收集不同类型的农作物病虫害图像数据集,包括正常植物图像和不同病虫害类型的图像。 数据集需要足够大且具有代表性,涵盖多种病虫害类型和不同的生长阶段。 数据预处理: 对图像进行预处理,如缩放、裁剪、灰度化等,以便后续的特征提取和模型训练。 特征提取: 使用预训练的深度学习模型(如 ResNet、VGG、Inception 等)提取图像的特征向量。 也可以使用传统的图像处理方法提取图像的纹理、形状、颜色等特征。 模型训练: 使用机器学习或深度学习模型对提取的特征进行训练,以实现病虫害的识别分类。 对于深度学习模型,可以使用 TensorFlow、Keras 或 PyTorch 等框架进行训练。 模型评估: 使用测试数据集对训练好的模型进行评估,计算准确率、精确率、召回率等指标,评估模型的性能。 部署应用: 将训练好的模型集成到应用程序中,实现实时的病虫害识别分类功能。 可以使用 Flask、Django 等 Web 框架搭建后端服务,实现与用户交互的界面。 技术和工具: Python:作为主要编程语言,用于数据处理、模型训练和应用开发。 深度学习框架:如 Tensor
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收起资源包目录
SYS.zip (61个子文件)
Plant_Disease_Detection-master
.github
CODEOWNERS 295B
ISSUE_TEMPLATE
feature_request_template.md 613B
bug_report_template.md 699B
demo.jpg 161KB
pull_request_template.md 1KB
workflows
greetings.yml 392B
FUNDING.yml 697B
app
server.py 3KB
view
2 (3).PNG 1.49MB
1 (3).PNG 1.48MB
4 (3).PNG 1008KB
index.html 3KB
models
models.md 252B
export_resnet34_model.pkl 83.45MB
static
style.css 4KB
SS.png 1.31MB
logo.png 4MB
th.jpg 31KB
bgd.jpg 103KB
logoWB.png 170KB
client.js 1KB
Plant_Disease_Detector_logo(With white background).png 102KB
img.jpg 77KB
Plant_Disease_Detector_logo(Transparent background).png 99KB
leaf.png 601B
LICENSE 35KB
th.jpg 31KB
CONTRIBUTING.md 5KB
notebook
Plant_Disease_RESNET50.ipynb 258KB
Plant_Detect_PyTorch.ipynb 184KB
Plant_Disease_Detection_TensorFlow.ipynb 1.6MB
Plant_Disease_Detection_Fastai.ipynb 949KB
Plant_Disease_Detection_Keras.ipynb 1.05MB
plant_disease_detector.ipynb 1.87MB
Plant_Disease_DenseNet121.ipynb 306KB
Plant_Disease_VGG19.ipynb 243KB
Plant_Disease_VGG16.ipynb 250KB
Dockerfile 312B
CODE_OF_CONDUCT.md 3KB
requirements.txt 174B
app.yaml 89B
deployment_guide
local_flask
templates
index.html 2KB
requirements.txt 57B
webapp.py 3KB
static
style.css 3KB
bgd.jpg 103KB
client.js 1KB
img.jpg 77KB
leaf.png 601B
README.md 933B
aws_deployment.md 4KB
gcp_deployment.md 5KB
images
aws-app-init.png 45KB
gcp-console-example.png 22KB
aws-deployed-banner.png 63KB
gcp-create-project.png 16KB
gcp-open-console.png 13KB
.gitignore 19B
.dockerignore 56B
README.md 5KB
th.jpg 31KB
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