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B.E. Project Report
B.E. Agricultural Monitoring and Crop Prediction System with Machine Learning Report
Get structured B.E. Agricultural Monitoring and Crop Prediction System with Machine Learning Report Final Year Project Report and Documentation with project objectives, methodology, system design, diagrams, implementation details, testing and complete project explanations. Suitable as a learning, documentation and project-presentation resource for students working on related final year projects.
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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Abstract
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Table of Content
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01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
AgriMonitor Pro is a Flask web application for smart agricultural monitoring, crop recommendation, yield prediction, and risk classification using machine learning. The system is designed for farmers and administrators to manage farms, record crop and soil data, train ML models locally, generate predictions, and download reports in CSV and PDF formats.
This agriculture management system uses Python 3, Flask 3, SQLAlchemy, SQLite, pandas, and scikit-learn. It supports Random Forest classification and regression, dataset management, user management, farm monitoring, soil health tracking, analytics dashboards, and report generation with Matplotlib and ReportLab.
The platform provides a guided farmer portal for adding farms, entering NPK and weather values, checking crop health, estimating yield, and reviewing prediction history. It also includes a powerful admin panel for managing users, datasets, model training, notifications, feedback, and data exports.
This project is suitable for agriculture technology, farm management software, smart farming solutions, precision agriculture systems, and machine learning based crop advisory platforms
Login Credentials
Administrator
- Username:
admin - Password:
admin123
Demo Farmer Users
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Generated after running:
python seed_data.py
- Default password for seeded farmers:
farmer123
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- Admin login and dashboard
- User management with create, edit, view, activate, deactivate, and delete options
- Farm management for all registered users
- Dataset upload, preview, archive, restore, and training selection
- Data preprocessing with missing value and duplicate inspection
- Machine learning model training for crop recommendation, yield estimation, and risk prediction
- Default model selection for crop, yield, and risk modules
- Model metrics view and trained model management
- Global crop monitoring record management
- Soil, yield, and risk record management
- Prediction record management with update and delete options
- Parameter range management for agricultural input validation
- Feedback management with status update, resolve, reopen, and delete
- Notification and notice management
- Analytics and aggregated reporting dashboard
- CSV and PDF export reports
- Database backup workflow
- Farmer signup and login system
- User dashboard with farm overview and activity summary
- Profile management with password, security question, contact details, and profile image
- Farm creation, editing, and deletion
- Crop monitoring entry for N, P, K, temperature, humidity, rainfall, soil moisture, pH, season, crop, and yield
- Data validation using admin-defined parameter ranges
- View active training dataset
- Trigger machine learning model training from the user portal
- Crop prediction based on soil nutrients and climate data
- Soil health monitoring with rule-based condition labels
- Yield estimation using trained regression model
- Agricultural risk classification using trained ML model
- Monitoring history management
- Prediction history view
- Personal report generation with chart views
- Download personal CSV and PDF reports
- Feedback submission to administrators
- Built with Flask 3 and Python 3
- Uses SQLAlchemy ORM with SQLite database by default
- Supports local machine learning model training with scikit-learn
- Random Forest classifier and regressor integration
- Pickle model storage under
instance/models/ - Automatic creation of required folders and database on first launch
- Default admin account generation
- Default parameter range generation
- Dataset fallback using
data/crop_recommendation.csv - Kaggle dataset compatible crop recommendation workflow
- Extended support for yield and risk columns
- Report generation using ReportLab and Matplotlib
- CSV cleaning and export support
- Suitable for academic, portfolio, internship, and final year project use
- Clone or download the project.
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Create a virtual environment:
python -m venv .venv
.\.venv\Scripts\Activate.ps1 -
Install dependencies:
pip install -r requirements.txt
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Run the Flask application:
python app.py
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Open in browser:
http://127.0.0.1:5000
Optional environment variables
SECRET_KEYfor Flask session securityDATABASE_URLfor custom database connectionADMIN_PASSWORDto set initial admin password before first database creation
Seed demo data
python seed_data.py
README Included
Setup Support Available
Administrator
- Username:
admin - Password:
admin123
Demo Farmer Users
-
Generated after running:
python seed_data.py
- Default password for seeded farmers:
farmer123