Automated Drip Irrigation System

Automated Drip Irrigation System

Project Overview

Collection of machine learning models designed to provide crop and fertilizer recommendations. By leveraging supervised learning algorithms and training data, Agro-AI delivers precise and actionable insights for agricultural decision-making.. It integrates machine learning models with real-time data infernce to predict trends and optimize nutrient dispersion effectively. Additionally, this repository includes models from the Agro-AI project, which provides crop and fertilizer recommendations based on supervised learning algorithms and robust data analysis.

Test out the model yourself: Deployment Portal.

Key Features

  • Data-driven decision-making using supervised and unsupervised algorithms.
  • Real-time trend analysis and prediction models.
  • Customizable agent configurations for diverse farming senarios and production gaols.
  • Comprehensive visualization tools for model performance insights.

Project Gallery

figure 1: schematics Diagram

figure 1: Schematics Diagram

figure 2: Early Prototype System

figure 2: Early Prototype System

figure 3: Test Prototype

figure 3: Test Prototype

figure 4: Greenhouse Concept

figure 4: Greenhouse Concept

figure 5: Deployment Concept

figure 5: Deployment Concept

Technologies Used

  • Programming: Python
  • Machine Learning Frameworks: TensorFlow, PyTorch
  • Data Processing Tools: Pandas, NumPy
  • Visualization: Matplotlib, Plotly
  • Algorithms for Crop Recommendation: FCM (Fuzzy C-Means), PCR (Principal Component Regression), RF (Random Forest), SVR (Support Vector Regression)
  • Algorithms for Fertilizer Recommendation: Linear Regression, Neural Networks, RF (Random Forest), SVR (Support Vector Regression)
  • Version Control: Git, GitHub

Future Enhancements

The project aims to incorporate dynamic knowledgebase to predict better crop and fertilizer recommendations based on historical data and current trends. Future iterations will also focus on improving scalability and response times. For Agro-AI, enhancements include expanding the database for diverse regional crops and fertilizers, integrating weather prediction models, and adding real-time sensor data analysis for better recommendations.