YIELTOR: Crop Prediction and Analysis Web Application

Yieltor is a web application designed to assist farmers and agricultural experts by predicting crop yields and providing detailed analysis based on various machine learning models. The application allows users to input relevant data, submit it for analysis, and receive both a visual display of the results and an audible response.

Home Page

Input Fields

Result

A voice is played as speech saying about whether the soil is good or bad and also says about best crop

Features

🌱 Crop Prediction

• Input Data: Users can input various parameters related to soil, weather, and other environmental factors.
• Multiple Models: The application utilizes several machine learning models, including:
  • Naive Bayes (NVB)
  • Passive Aggressive Classifier (PAC)
  • Support Vector Machine (SVM)
  • Random Forest (RF)

🎙️ Voice Feedback

• Audible Results: Once the prediction or analysis is complete, the results are spoken aloud using text-to-speech technology.
• Visual Display: In addition to the voice feedback, results are also displayed on the screen for easy reference.

📁 File Upload & Analysis

• Results Display: The processed results are shown on the screen and also conveyed through voice feedback.

Getting Started

To get started with the Yeiltor project, clone the repository and follow the instructions below to set up your development environment.

Prerequisites

• Python 3.x
• Django 2.x
• SQLite (or your preferred database)
• Any required Python packages as listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/soil-kropter.git
   cd soil-kropter

2. Install dependencies:

   pip install -r requirements.txt
   pip install pandas
   pip install emoji
   pip install --upgrade pip setuptools
   pip install matplotlib
   pip install scikit-learn
   pip install seaborn
   pip install pyttsx3
   pip install pymysql
   pip install openpyxl

3. Apply migrations:

   python manage.py migrate

4. Run the development server:

   python manage.py runserver

5. Access the application: Open your browser and navigate to http://127.0.0.1:8000/.

Usage

• Input Data: Fill in the required fields on the form and submit.
• View Results: Listen to the voice feedback and check the visual results on the screen.
• Explore Models: Experiment with different machine learning models to see how they perform on your data.

Future Scope

• Fertilizer Recommendation: Based on the crop prediction, the app also provides fertilizer recommendations.
• CSV/XLSX Upload: Users can upload CSV or Excel files containing relevant data for bulk analysis.
  🛠️ User Management
• User Authentication: The app includes user registration and login features to ensure secure access.
• Dashboard: After logging in, users are presented with a dashboard where they can access various tools and features.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.