utpalpaul108/plant-disease-detection

Image-based plant disease detection systems hold immense promise for revolutionizing agriculture by enabling early detection, precise control, and sustainable management of plant diseases, leading to increased crop yields, improved food quality, and enhanced economic benefits for farmers.

Plant Disease Detection 🚀

Examples

 

Workflows

1. Update config.yaml
2. Update secrets.yaml [Optional]
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src config
6. Update the components
7. Update the pipeline
8. Update the main.py

Dataset

• You can use any dataset (including the Kaggle dataset) that is supported by opendatasets. Just place the URL of the dataset in config/config.yaml/data_ingestion/source_URL
• If you want to use your custom dataset, just place it inside the artifacts/data_ingestion/dataset folder or any other custom folder that you have to mention in config/config.yaml/data_ingestion/dataset_dir

Steps to run

STEP 00 : Clone the repository

https://github.com/utpal108/waste-detection-using-yoloV5

STEP 01 : Create a virtial environment after opening the repository

Using Anaconda Virtual Environments

conda create -n venv python=3.10 -y
conda activate venv

Or for Linux operating system, you can use that

python3.10 -m venv venv
source venv/bin/activate

STEP 02 : Install the requirements

pip install -r requirements.txt

For downloading the Kaggle dataset, place the kaggle.json file (Kaggle API credentials) in your root directory.

Finally, run the following command to run your application:

python app.py

STEP 03 : Run the application

Now,open up your local host with a port like that on your web browser.

http://localhost:8080

STEP 04 : Train the model

Before predicting, you have to train your model with your own dataset.

http://localhost:8080/train

After completing the training, you can now detect any plant disease from the plant leaf image, according to your training datasets.

AWS CICD Deployment With Github Actions

STEP 00 : Login to AWS console.

STEP 01 : Create IAM user for deployment

#with specific access

1. EC2 access : It is virtual machine

2. ECR: Elastic Container registry to save your docker image in aws


#Description: About the deployment

1. Build docker image of the source code

2. Push your docker image to ECR

3. Launch Your EC2 

4. Pull Your image from ECR in EC2

5. Lauch your docker image in EC2

#Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

STEP 02 : Create ECR repo to store/save docker image

- Save the URI: 681776806933.dkr.ecr.us-east-2.amazonaws.com/plant-disease-detection

STEP 03 : Create EC2 machine (Ubuntu)

STEP 04 : Open EC2 and Install docker in EC2 Machine:

#optinal

sudo apt-get update -y

sudo apt-get upgrade

#required

curl -fsSL https://get.docker.com -o get-docker.sh

sudo sh get-docker.sh

sudo usermod -aG docker ubuntu

newgrp docker

STEP 05 : Configure EC2 as self-hosted runner: setting>actions>runner>new self hosted runner> choose os> then run command one by one

STEP 06 : Setup github secrets:

AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION = us-east-2

AWS_ECR_LOGIN_URI = demo>>  681776806933.dkr.ecr.us-east-2.amazonaws.com

ECR_REPOSITORY_NAME = plant-disease-detection