FarmApp is a simple Machine Learning and Deep Learning based application that helps farmers to improve the farming strategy. The application is capable of, predicting the best crop to be cultivated for that land, predicting the best fertilizer to be used for that crop and detecting various diseases of the crop. We use state-of-the-art machine learning and deep learning technologies to make the prediction/detection pretty accurate.
This is a POC(Proof of concept) kind-of project. The data used here comes up with no guarantee from the creator. So, don't use it for making real time farming. If you do so, the creator is not responsible for anything. However, this project presents the idea that how we can use ML/DL into farming if developed at large scale and with authentic and verified data.
Most of the datasets are uploaded in the "Dataset" folder except Crop Disease Detection dataset(1gb).
Crop recommendation dataset - Dataset/Crop_recommendation.csv
Fertilizer recommendation dataset - Dataset/Fertilizer prediction.csv
Crop Disease detection dataset - https://www.kaggle.com/vipoooool/new-plant-diseases-dataset
All the notebooks are uploaded in the "Notebooks" folder.
Crop recommendation - Notebooks/crop_recommendation.ipynb
Fertilizer recommendation - Notebooks/fertilizer_recommendation.ipynb
Crop disease detection - Notebooks/crop-disease-prediction.ipynb
You can view all the saved models (pickle files) in the "Saved_models" folder.
- Python (3.8 version)
- Html
- CSS
- Javascript
- Sklearn
- Matplotlib
- Numpy
- Pandas
- Flask
- Tensorflow
- Heroku
This website is deployed at Heroku.
You can access it here.
Clone the project
git clone https://github.com/Chandradithya8/FarmApp.git
Extract all the files.
Open command prompt and go to the extracted files directory
Go to "FarmApp-master" directory
cd FarmApp-master
When you type "dir" in the command prompt you should see the following files
- Dataset
- Notebooks
- Saved_models
- app.py
- CONTRIBUTING.md
- LICENSE
- requirements.txt
- etc
Install dependencies
pip install -r requirements.txt
Start the server
python app.py
Then you will be able to use the application on your localhost.
- In this application I have included two machine learning and one deep learning model. In the future I will try to add even more models.
You can use this project for further developing it and adding your work in it. If you use this project, kindly mention the original source of the project and mention the link of this repo in your report. Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
This project is licensed under GNU (GENERAL PUBLIC LICENSE).
If you have any doubt or want to contribute feel free to hit me up on LinkedIn