AI-powered farm management system using Python
AI-Powered Farm Management System is a python based project. We have developed Farm Management System using Python Django SQLite, Bootstrap 5, Chart.js, scikit-learn, and NLTK . The main modules available in this project are manage different farming activities, including crop production, irrigation, livestock, employees, machinery, inventory, sales, and farm finances.
Built with Django, a powerful Python framework, and SQLite as a database manager, the website is deployable, secure and based platform that integrates farm management tools with a crop marketplace, enabling farmers to manage resources efficiently
Project Overview
| Project Details | Information |
|---|---|
| Project Name | AI-Powered Farm Management System |
| Programming Language | Python |
| Framework | Django |
| Database | SQLite |
| Frontend | Bootstrap 5 |
| Charts | Chart.js |
| AI and Machine Learning | scikit-learn, NLTK |
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What Are the Main Features of AI-Powered Farm Management System?
1. Admin Panel
AI-Powered Farm Management System includes a custom-built administration panel instead of Django’s default admin interface. It provides 35 management modules with CRUD operations, search, filters, date ranges, sorting, pagination, detail modals, delete confirmation, CSV export, and CSV import.
The modules cover:
- Farms, fields, crops, irrigation, fertilizer, pesticide, and harvests.
- Animals, health, vaccinations, feeding, veterinary visits, breeding, and livestock production.
- Employees, tasks, attendance, payroll, performance, and schedules.
- Machinery, maintenance, fuel, usage, inventory, stock movements, and suppliers.
- Purchase orders, buyers, sales, invoices, payments, expenses, users, and settings.
2. Multiple User Roles
The application provides four roles with different access permissions.
- Admin: Complete application access.
- Manager: Access to all modules except users and settings.
- Farm Owner: Access only to their own farm data.
- Employee: Access to personal tasks, attendance, salary, schedules, and performance.
3. Separate Dashboards
AI-Powered Farm Management System provides separate dashboards for administrators, farm owners, managers, and employees. The dashboards include clickable KPI cards and Chart.js graphs. Dashboard values are aggregated from database records.
4. Finance Management
- Sales and printable invoices.
- Payment records and outstanding balances.
- Expense management.
- Profit and loss reports per crop and farm.
- Financial reports with CSV export.
- Automatic expense records for specified operational activities.
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5. Inventory Management
- Automatic stock updates through stock movements.
- Stock reversal when movements are deleted.
- Prevention of excessive stock withdrawal.
- Low-stock notifications.
- Stock additions from received purchase orders.
6. AI Chatbot
The AI chatbot uses TF-IDF, NLTK Porter stemming, and a Logistic Regression intent classifier with 28 intents. It includes confidence-based fallback, conversation history, analytics, and role-based answers generated from live database records.
7. AI-Powered Insights
The insights section provides crop production analysis, milk production forecasts, monthly harvest and expense forecasts, stock-depletion predictions, and rule-based recommendations.
Actual database records are displayed with green labels, while machine learning predictions use amber labels and dashed chart lines.
Technology Stack
| Technology | Purpose |
|---|---|
| Python | Application programming |
| Django | Backend web framework |
| SQLite | Database management |
| Bootstrap 5 | Frontend interface |
| Chart.js | Dashboard graphs |
| scikit-learn | Machine learning |
| NLTK | Natural language processing |
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How to Download
The complete package is available so you can run, study, and submit it with confidence.
Screenshot
How to Install AI-Powered Farm Management System Using Django?
Follow these steps to configure and run AI-Powered Farm Management System on Windows using PowerShell and Visual Studio Code.
- Open the Project Folder in vs code.
- Create a Virtual Environment using this cmd “python -m venv venv”
- Activate the Virtual Environment using this cmd “.\venv\Scripts\Activate.ps1”
- Install All Dependencies using this cmd “python -m pip install –upgrade pip “ and “pip install -r requirements.txt”
- Generate Migrations using this cmd “python manage.py makemigrations accounts farm chatbot”
- Apply Migrations using this cmd “python manage.py migrate”
- Create Demo Data using this cmd “python manage.py seed_demo”
- Run the Project “python manage.py runserver”
Demo Login Credentials
| Role | Username | Password |
|---|---|---|
| Admin | admin | Admin@123 |
| Manager | manager | Manager@123 |
| Farm Owners | owner1 to owner5 | Owner@123 |
| Employees | emp01 to emp24 | Employee@123 |
Change the demo passwords before using the application in a real deployment. Farm owners can also register through /accounts/register/.
AI Chatbot Training
The chatbot model is trained automatically when demo data is seeded. Manual training can be performed using:
python manage.py train_chatbot
Alternatively:
python chatbot/ml/train.py
Example chatbot questions include low-stock items, milk production today, vaccinations due, total expenses last month, profit this year, outstanding payments, and stock depletion forecasts. Employees can also ask about their tasks and salary.
Frequently Asked Questions (FAQs)
1. What is AI-Powered Farm Management System?
AI-powered farm management application developed using Django and SQLite.
2. Which technologies are used?
It uses Django, SQLite, Bootstrap 5, Chart.js, scikit-learn, and NLTK.
3. Does AI-Powered Farm Management System have a custom admin panel?
Yes. It uses a custom-built admin panel instead of Django’s default admin interface.
4. How many management modules are included?
The custom administration panel includes 35 management modules.
Final Thoughts
AI-Powered Farm Management System is a comprehensive academic project combining Django web development, farm administration, financial management, inventory tracking, and machine learning features.
