🌾 AgroSafeAI

🤖 AI-Powered Smart Agriculture Decision Support System

📘 Course: ITEP 308 – System Integration and Architecture I
🗓 Academic Term: First Semester, Academic Year 2025–2026
🏫 Section: 3WMAD-1

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📌 Project Overview

AgroSafeAI is a web-based smart agriculture decision support system designed to help farmers 👨‍🌾 and agricultural administrators make accurate, data-driven decisions.

The system focuses on:

• 🌱 Crop disease diagnosis
• 💊 AI-trained treatment planning
• 📊 Smart prediction and analysis
• ⏱ Real-time insights using live weather and market data

By integrating machine learning, real-time APIs, and database-driven systems, AgroSafeAI aims to reduce crop loss, optimize resource usage, and support sustainable farming practices.

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🎯 Objectives

• 🔍 Detect potential crop diseases early
• 🧠 Generate AI-based treatment plans
• 📈 Provide real-time predictions and smart insights
• 💰 Support cost-aware and ROI-driven decisions
• 🧩 Demonstrate full system integration using modern web technologies

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🧠 Key Features

🌱 Disease Diagnosis

• AI-based crop disease classification
• Early detection to prevent severe crop damage

💊 AI-Trained Treatment Plan

• Automatic generation of treatment recommendations
• Fungicide suggestions based on trained AI models

📊 Smart Prediction and Analysis

• Water usage prediction
• Risk-aware decision support
• AI-driven insights for better farming decisions

🌦 Real-Time Environmental Monitoring

• Live weather data integration using Weather API
• Supports disease and irrigation predictions

💰 Smart ROI and Market Insights

• Real-time market and currency data
• Cost-aware treatment planning
• Smart ROI-based decision support

👤 User and Admin System

• Secure user authentication and sessions
• Admin panel for system monitoring and model retraining

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🏗 System Architecture Overview

AgroSafeAI follows a layered system architecture :

1. 🖥 Frontend (User Interface)

   • HTML, CSS, JavaScript, Bootstrap
   • Responsive and user-friendly design

2. ⚙ Backend Application

   • PHP 8.x
   • Handles business logic and request processing

3. 🤖 Machine Learning Layer

   • PHP-ML (PHP-AI)
   • Trained models stored as .phpml files

4. 🗄 Database Layer

   • MySQL (via XAMPP)
   • Stores users, sessions, prediction history, and logs

5. 🌐 External APIs

   • Weather API for real-time environmental data
   • Market and Currency API for live price and trade data

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🛠 Technologies Used

⚙ Backend and Server

• PHP 8.x
• XAMPP (Apache + MySQL)
• Composer (dependency management)

🤖 Machine Learning

• PHP-ML (PHP-AI)
• Trained AI models:
  • 🌱 Disease Classifier
  • 💊 Fungicide Predictor
  • 💧 Water Predictor

🗄 Database and Storage

• MySQL
• 📄 CSV datasets for ML training

🎨 Frontend

• HTML
• CSS
• JavaScript
• Bootstrap

🌐 APIs

• 🌦 Weather API
• 💱 Market and Currency API

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Installation and Setup Instructions

1. Clone the Repository

git clone https://github.com/PaulPaolo2929/AgroSafeAI.git

2. Setup XAMPP

• Install XAMPP
• Start Apache and MySQL
• Move the project folder to:

htdocs/

3. Install Dependencies

Make sure Composer is installed, then run:

composer install

4. Database Setup

• Open phpMyAdmin
• Create a new MySQL database
• Import the SQL file (if provided)
• Update database credentials in:

includes/config.php

5. Train Machine Learning Models

• Run the training script:

php train.php

This will generate .phpml model files inside the models/ directory.

6. Run the System

Open your browser and go to: http://localhost/AgroSafeAI/

Live Deployment

User Login:

https://agrosafeai.infinityfreeapp.com/index.php

Admin Login:

https://agrosafeai.infinityfreeapp.com/admin/login.php

Presentation and Documentation

Final Presentation (Canva): https://www.canva.com/design/DAG7Yi5BJSY/8VfjaV3IFT8SyFWz4RcQHA/edit

Final Files and Video (Google Drive): https://drive.google.com/drive/folders/1Oe1kAQAojBfmmUTwVu4o3IQexz-6OTzM?usp=sharing

Project Members

Paul Paolo A. Mamugay

Kim Andrei Veloria

Mark Jesus Fidelino

Course Information

Course: ITEP 308 – System Integration and Architecture I Academic Term: First Semester, Academic Year 2025–2026

Future Enhancements

Mobile-friendly Progressive Web App (PWA)

SMS or email alerts

Expanded training datasets

Automated model retraining

Advanced analytics dashboard

Conclusion

AgroSafeAI demonstrates a complete system integration project combining web development, machine learning, real-time APIs, and database systems. It delivers a practical and intelligent solution for modern agriculture while meeting academic and enterprise-level requirements.