https://github.com/fatiq123/plant-disease-detection

Plant Disease Detection using Machine Learning with its popular framework Pytorch.
https://github.com/fatiq123/plant-disease-detection

ai jupyter-notebook machine-learning python3 pytorch

Last synced: about 4 hours ago
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Plant Disease Detection using Machine Learning with its popular framework Pytorch.

• Host: GitHub
• URL: https://github.com/fatiq123/plant-disease-detection
• Owner: fatiq123
• License: mit
• Created: 2024-01-24T04:50:36.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-01-24T05:02:56.000Z (10 months ago)
• Last Synced: 2024-01-24T06:25:36.269Z (10 months ago)
• Topics: ai, jupyter-notebook, machine-learning, python3, pytorch
• Language: Jupyter Notebook
• Homepage:
• Size: 8.52 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ⭐Plant-Disease-Detection

* Plant Disease is necessary for every farmer so we are created Plant disease detection using Deep learning. In which we are using convolutional Neural Network for classifying Leaf images into 39 Different Categories. The Convolutional Neural Code build in Pytorch Framework. For Training we are using Plant village dataset.

## ⭐Run Project in your Machine

* You must have python install in your machine.

* Create a Python Virtual Environment & Activate Virtual Environment [Link](https://docs.python.org/3/tutorial/venv.html)

* Install all the dependencies using below command

    `pip install -r requirements.txt`

* Go to the `Flask Deployed App` folder.

* Download the pre-trained model file `plant_disease_model_1.pt` from [here](https://drive.google.com/drive/folders/1ewJWAiduGuld_9oGSrTuLumg9y62qS6A?usp=share_link)

* Add the downloaded file in `Flask Deployed App` folder.

* Run the Flask app using below command `python3 app.py`

* You can also use downloaded file in `Model` Section and play with it using Jupyter Notebook.

## ⭐Testing Images

* If you do not have leaf images then you can use test images located in test_images folder

* Each Image have it's disease name so you can verify model is working perfact or not.

## ⭐Snippet of Web App :

#### Main page

 


#### AI Engine 

 


#### Results Page 

 


#### Supplements/Fertilizer  Store