CROPCARE
01 — overview
CROPCARE addresses critical operational gaps at the Center for Urban Agriculture and Innovation (CUAI) of Quezon City University, where existing crops are managed manually with limited monitoring of soil nutrients, moisture, and pH levels. This capstone project integrates IoT sensors, adaptive algorithms, and AI-driven insights to enable informed decisions on crop suitability, fertilizer requirements, and growth cycle management.
- Status
- Completed
- Duration
- Type
- Web App · IoT
02 — stack
JavaScript
Tailwind CSS
PHP
MySQL
Google Gemini AI
ESP32
MQTT
03 — key features
- Real-time soil health monitoring with NPK nutrients, pH levels, and moisture detection across multiple cultivation plots using IoT sensors
- AI-powered crop suitability analysis and fertilizer recommendations using Google Gemini AI based on real-time soil conditions
- Adaptive irrigation system with moisture-based control, schedule management, and rule-based automation for efficient water distribution
- Multi-plot management supporting independent crop assignments with growth cycle tracking, planting dates, and harvest monitoring
- Comprehensive web dashboard with role-based access control, real-time alerts, activity logging, and bilingual support for farm personnel
04 — contributors
Eeanne Drew D.C. Del Rosario
Project Manager
Miguel Enrique A. Dasalla
Lead Programmer
Mike Lyndon B. Bigcas
Full-Stack Developer
Daniele Jones N. Morales
System Analyst
Mark Anthony Evardo
Programmer
Shanlee N. Reyes
Programmer