SOUTHWAY COLLEGE OF TECHNOLOGY
(SOCOTECH)
San Francisco, Agusan del Sur, Philippines
TeleFax: (085) 839-4476/1170
www.socotech.edu.ph
“IoT-Based Pest and Disease Early Detection System for
Agriculture”
Project Title: CropGuard
"Guarding Crops, Growing Futures."
Prepared by: Jeffer John S. Sanchez
Course / Subject: BSIT 4 - CAP101
Date: [Presentation Date]
Project Title: CropGuard: IoT-Based Pest and Disease Early
Detection System for Agriculture
Project Description: "CropGuard"
CropGuard is a smart agricultural solution designed to protect
crops from pests and diseases using real-time data and
automation. The brand name signifies "guarding" or "protecting"
crops from biological threats through intelligent monitoring.
CropGuard combines IoT sensors and AI-driven image analysis to
help farmers identify early signs of plant diseases or infestations
and take preventive action, reducing crop loss and pesticide use.
Project Purpose or Function
The purpose of CropGuard is to minimize agricultural damage
caused by pests and diseases by providing an early warning
system through technology. The system continuously monitors
crop health conditions and provides alerts to farmers, allowing
timely interventions.
Key functions include:
Real-time visual monitoring of crops using camera modules
Detecting signs of disease (e.g., yellowing, wilting, spotting)
using image processing
Measuring temperature and humidity to predict pest activity
Sending alerts to farmers via mobile app or SMS
Optionally activating localized pesticide sprayers when
needed
By proactively identifying threats, CropGuard empowers farmers
to respond quickly, reduce chemical dependency, and protect
crop yield.
3. Project Platform
CropGuard is developed on an ESP32-based IoT platform with
support for image recognition and environmental monitoring. The
hardware and software components include:
Microcontroller: ESP32-CAM or Raspberry Pi (for enhanced
processing)
Camera Module: ESP32-CAM or Pi Camera for plant leaf
image capture
Sensors: DHT22 for temperature and humidity, optional UV
or gas sensors
AI Model: Lightweight image classification model (TensorFlow
Lite) for detecting crop diseases
Connectivity: Wi-Fi or GSM for cloud alerts and updates
Control Interface: Custom mobile/web dashboard for visual
reports and alerts
The system is programmed to capture plant images periodically
and analyze them using machine learning models trained to
recognize early symptoms of common crop diseases.
Environmental data is used to support pest outbreak forecasting.
Optional Features
Cloud storage of health data and image logs
Pesticide spraying module triggered by detection
Solar-powered operation for remote farms
Predictive alerts based on weather API and disease
databases
CropGuard offers a modern, low-cost, and scalable solution for
protecting crops in real-time, ensuring healthier yields and more
sustainable farming practices.