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https://github.com/sagnikbasak04/kisansahayak

KisanSahayak is a smart agriculture web application aimed at providing Indian farmers with data-driven insights using advanced machine learning, rainfall analysis, crop recommendations, and disease prediction. 🌱
https://github.com/sagnikbasak04/kisansahayak

android-application crop-recommender disease-prediction machine-learning marketplace multilingual-websites rainfall-data smart-agriculture

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KisanSahayak is a smart agriculture web application aimed at providing Indian farmers with data-driven insights using advanced machine learning, rainfall analysis, crop recommendations, and disease prediction. 🌱

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README 

        
# KisanSahayak - Empowering Farmers with Smart Agriculture Solutions



**KisanSahayak** is a smart, data-driven web application designed to help farmers make informed decisions. By integrating machine learning, environmental data, advanced imaging techniques, and innovative marketplace features, we aim to provide farmers with real-time insights into rainfall patterns, crop recommendations, disease management, and more.



## Table of Contents

- [Project Overview](#project-overview)

- [Features](#features)

- [Technologies Used](#technologies-used)

- [Getting Started](#getting-started)

- [Usage](#usage)

- [Mobile App Usage](#app-usage)

- [Dataset Information](#dataset-information)

- [Future Work](#future-work)

- [Team Members](#team-members)

- [License](#license)



## Project Overview

KisanSahayak offers farmers a comprehensive solution for smarter agricultural practices, utilizing cutting-edge technologies such as machine learning and computer vision. Key highlights include:

- **Rainfall & Climate Analysis:** In-depth analysis of district-wise rainfall patterns and climate variations to help farmers plan their sowing and irrigation strategies.

- **Crop Recommendations:** AI-based suggestions for optimal crops to grow, based on soil nutrients, temperature, and humidity.

- **Disease Prediction & Management:** Our system predicts potential crop diseases and offers management tips, using both traditional and advanced methods like hyperspectral imaging.

- **Voice Assistance:** A voice-enabled system that allows farmers to interact with the app hands-free, making it user-friendly for all.

- **Image Analysis for Disease Detection:** Farmers can upload photos of crops, and the app selects the best out of 3 images to analyze for disease detection.

- **Multilingual Support:** Farmers can access the platform in multiple regional languages, ensuring ease of use for everyone.

- **Trusted User Marketplace:** A secure, verified platform for farmers to sell or buy products from other farmers or retailers.

- **Disease Prediction:** The app predicts the most likely disease in a farm based on current environmental and crop conditions.



KisanSahayak aims to simplify agricultural decision-making, reduce risks, and improve yields for farmers across India.



## Features

- **Rainfall Analysis:** Understand actual vs. normal rainfall and deviation trends.

- **Crop Recommendations:** AI-based suggestions for ideal crops to grow in specific environmental conditions.

- **Disease Prediction:** Forecast potential crop diseases and offer actionable management tips.

- **Interactive Reports:** Generate detailed analysis reports from user-uploaded data.

- **Voice Assistance:** Voice-controlled features for ease of use.

- **Best Camera Angle Selection:** Automated selection of the best image out of three for better accuracy in disease detection.

- **Multilingual Support:** Access the app in various regional languages for easy navigation.

- **Trusted User System:** Farmers and retailers are verified to maintain trust and transparency in the marketplace.

- **Marketplace:** A platform for farmers to trade goods with each other or retailers, ensuring fair access to resources.

- **Most Likely Disease Prediction:** AI-powered predictions on the most probable diseases affecting the crops in a specific farm.



## Technologies Used

- **Backend:** Node JS

- **Frontend:** HTML, CSS, JavaScript, React, React Native

- **Machine Learning:** scikit-learn, pandas, TensorFlow

- **Database:** MongoDB

- **Deployment:** AWS EC2, Docker

- **Additional Technologies:** NLP for voice recognition, image processing for camera selection, multilingual integration.



## Getting Started

To get started with the project, clone the repository and install the necessary dependencies.



```bash

git clone https://github.com/yourusername/kisansahayak.git

cd kisansahayak

cd backend

npm install

cd ../frontend

npm install

cd ../ML

pip install -r requirements.txt

```



## Usage

Once the project is set up, you can start the web application using the following command on your root directory:



```bash

cd frontend

npm run dev

cd ../backend

npm start

cd ../ML

uvicorn app:app --reload

```



Open your browser and navigate to `http://localhost:5173/` to use the application.



## App Usage

You can also start the mobile application using the following command on your root directory:



```bash

cd android/frontend

npx expo start

cd ../../backend

npm start

cd ../ML

uvicorn app:app --reload

```



Download Expo Go app on your mobile device



- [Download for Android (Google Play Store)](https://play.google.com/store/apps/details?id=host.exp.exponent)

- [Download for iOS (App Store)](https://apps.apple.com/app/expo-go/id982107779)



After the app has started running, a QR code will be generated in your terminal or browser. Open the Expo Go app on your mobile phone and use the built-in camera or Expo Go to scan the QR code. Once scanned, the mobile app will load directly in the Expo Go app.



## Dataset Information

Our data is sourced from reliable datasets like IMD (India Meteorological Department) and district-wise agricultural reports. The data contains key features like district names, actual rainfall, normal rainfall, percentage deviation from the norm, soil nutrients (NPK), temperature, and humidity.



## Future Work

We are actively working on enhancing KisanSahayak by introducing:

- **AI-powered AR Image Assistance:** While capturing crop photos, the app will use augmented reality (AR) to guide farmers on the optimal way to take the picture for disease detection.

- **Hyperspectral Reflectance Method:** We are exploring hyperspectral imaging techniques to predict crop diseases more accurately, leveraging the reflectance properties of leaves and plants.

- **Expanded Crop and Disease Prediction Models:** Building more robust models to predict diseases in a broader range of crops and environmental conditions.



These features are in development and will be added to future versions of KisanSahayak.



## Team Members

This project is a collaborative effort by:

- **Sagnik Basak** (Team Leader) - Machine Learning & Data Analysis Development

- **Tamojit Das** – Full Stack Development & Application System Design

- **Ankan Das** – DL Model Design & Development

- **Debeshee Sen** – Full Stack Development & UI/UX Design

- **Titas Kabiraj** – Front End, UI/UX Design & Documentation

- **Ritesh Das** – Android Development & Full Stack Development



For inquiries, feel free to contact us via [[email protected]](mailto:[email protected]).



## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.



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**Note:** This is a private repository, and we are not currently open to contributions. However, we will be happy to consider your ideas in the future. Stay tuned!