IntelliFarm Tech

Abstract

IntelliFarm Tech is a precision farming solution designed to revolutionize modern agriculture by leveraging machine learning, Python, and Streamlit. The project aims to provide farmers with real-time, data-driven insights to enhance crop yield, optimize resource utilization, and foster sustainable farming practices.

Home Page of the project

Data Info Page of the Project

Table of Contents

• Abstract
• Problem Statement
• Aim and Objectives
• System Design/Architecture
• System Development Approach (Technology Used)
• Algorithm
• Analysis and Planning
• Data Preparation
• Machine Learning Models
• User Interface
• Testing and Deployment

Problem Statement

Agricultural communities face challenges such as suboptimal crop yields, diminished returns on investment, and a lack of informed decisions for veteran farmers regarding suitable crops.

Visualize The Crop

Aim and Objectives

• Aim: To empower farmers through data-driven insights and technology.

• Objectives:

  1. Provide data-driven insights.
  2. Suggest crops, fertilizers, and assess soil quality.
  3. Develop a user-friendly UI for farmers.
  4. Make the platform free of cost for broader accessibility.

System Design/Architecture

IntelliFarm Tech integrates Python and machine learning at the backend, with Streamlit serving as the GUI.

Predicting The Crop

System Development Approach (Technology Used)

• Algorithm:
  • Logistic Regression
  • Random Forest Classifier
  • Scikit Learn
  • Matplotlib
  • NumPy and Pandas

Analysis and Planning

Identify farmer needs, define scope, allocate resources, and set timelines.

Data Preparation

Gather agricultural data (weather, soil, crops), clean, preprocess, and organize the data.

Machine Learning Models

Develop crop, fertilizer, and soil analysis algorithms, train models using historical and real-time data.

User Interface

Create a farmer-friendly Streamlit dashboard, design an intuitive navigation system.

Testing and Deployment

Validate model accuracy through testing data, deploy the system on Azure.

Outcome Of The Predicted Soil/Crop In The Project

Thank you so much