SmartIrrigate

An AI-powered irrigation scheduling system designed to optimize water usage in agriculture by leveraging machine learning, data analytics, and cross-language support including R and PySpark.

🌱 Overview

This project aims to enhance agricultural productivity by providing intelligent irrigation recommendations.
By analyzing environmental and crop data, the system determines optimal watering schedules, ensuring efficient water utilization and promoting sustainable farming practices.

🧠 Features

• Machine Learning Models: Implements both teacher and student models using PyTorch and ONNX for efficient inference.
• Data Analysis: Utilizes Jupyter notebooks and R scripts for exploratory data analysis to understand and preprocess the dataset.
• Big Data Processing: Employs PySpark for training large-scale teacher models.
• API Service: Provides a FastAPI-based backend to serve model predictions.
• Docker Integration: Includes Docker support for containerized deployment.
• Streamlit GUI: Offers a GUI for user to specify sensor readings and recieve prediction.
• Power BI Dashboard: Offers a Power BI report for visualizing irrigation schedules and model insights.

📁 Project Structure

├── api/
│   └── app.py                      # FastAPI application entry point
├── app/
│   ├── __init__.py                 # Package initializer
│   ├── eda-aihtproject.ipynb       # Exploratory data analysis notebook
│   ├── frontend.py                 # Frontend logic for user interface
│   ├── model_train_and_export.py  # Model training and export utilities
│   ├── model_utils.py              # Model utility functions
│   ├── pyspark_teacher.py         # PySpark-based teacher model processing
│   └── teacher.py                 # Teacher model inference logic
├── artifacts/
│   ├── student_model.onnx          # ONNX format student model
│   ├── student_model.pt            # PyTorch format student model
│   └── teacher_model.pt            # PyTorch format teacher model
├── dataset/
│   └── Irrigation Scheduling.csv   # Dataset file for irrigation scheduling
├── Hardware Simulation/
│   └── app.py                     # Simulation application for hardware testing
├── tests/
│   ├── test_input.txt              # Sample input data for testing
│   └── test_model.py               # Unit tests for model functionalities
├── Dockerfile                     # Docker configuration for containerization
├── README.md                      # Project overview and documentation
├── irrigation_aiht_powerbi.pbix   # Power BI report for irrigation analysis
├── requirements.txt               # Python dependencies
└── run_instructions.md            # Instructions for running the application

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• Docker (for containerized deployment)
• R (for R-based data analysis)
• Apache Spark (for running PySpark)
• Streamlit (for running GUI)

Installation

1. Clone the repository

   git clone https://github.com/OnePunchMonk/AIHT-Project.git
   cd AIHT-Project

2. Create and activate a virtual environment

   python -m venv venv
   source venv/bin/activate      # On Windows: venv\Scripts\activate

3. Install Python dependencies

   pip install -r requirements.txt

4. Install R packages (for R analysis)

   install.packages("tidyverse")
   install.packages("data.table")

🏋️‍♂️ Training and Exporting the Model

• Train and export to ONNX

  python model_train_and_export.py

• Run the teacher model using PySpark

  spark-submit pyspark_teacher.py

🐳 Docker Deployment

1. Build the Docker image

   docker build -t irrigation-model .

2. Run the Docker container

   docker run -p 8000:8000 irrigation-model

🧪 API Testing

You can test the prediction endpoint using curl or any API testing client:

curl -X POST http://localhost:8000/predict   -H "Content-Type: multipart/form-data"   -F "file=@test_input.txt"

📊 Data Analysis

• eda-aihtproject.ipynb: R script for additional statistical analysis and visualizations. (Run on Kaggle env)

📈 Visualization

• irrigation_aiht_powerbi.pbix: Power BI dashboard for irrigation schedules and model insights.
• frontend.py: GUI for non-technical users.

🔗 Wokwi Simulation

For a hardware simulation of the irrigation system, refer to the AIHT Wokwi Simulation Repository.

• Or refer Deployment

Streamlit Deployment on HF Spaces

• Refer this link

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Note: Ensure all environment variables and configurations are set appropriately before deploying the application.