EcoSentry
2024
EcoSentry Abstract
An autonomous greenhouse system integrating multiple sensor types and AI-powered diagnostics. The system combines soil moisture monitoring, DHT temperature and humidity sensing, computer vision, and Arduino microcontroller logic to create a fully automated plant care solution. The AI model was trained on a custom basil leaf dataset to classify plant health states.
Sensor Integration
The EcoSentry system integrates multiple sensors into a cohesive monitoring network. The soil moisture sensor provides calibrated readings that trigger the self-watering system when thresholds are reached. The DHT sensor monitors ambient temperature and humidity, displaying real-time environmental data on an OLED screen via I2C communication protocol.
AI-Powered Plant Health Classification
A computer vision system was integrated with Google AI to classify plant health. The model was trained on a curated dataset of basil leaf images showing healthy, nutrient-deficient, and diseased states. The camera module captures images at regular intervals, and the AI model processes them to provide health diagnostics displayed on the OLED interface.
Autonomous Control Logic
The Arduino microcontroller runs threshold-based control algorithms to maintain optimal growing conditions. When soil moisture drops below calibrated setpoints, the system activates the watering mechanism. Environmental data is continuously logged and displayed, allowing users to track growing conditions over time and optimize plant care strategies.