AI-Driven Precision Aeroponics: Deep Learning for Plant Identification
and Health Monitoring in an IoT-Enabled System
Abstract
In the face of escalating global population, diminishing arable land,
and an increasing demand for organic food, innovative farming systems
are imperative. Traditional farming, characterized by excessive chemical
usage, poses health and environmental concerns. Addressing these
challenges, this research introduces a sophisticated system that
amalgamates image processing, deep learning, and precision nutrient
dosing for efficient soil-less farming. Utilizing a dataset of 24 GB,
encompassing 4187 images of 11 plant varieties across three growth
stages, a deep learning model was developed for automatic plant species
identification, health assessment, and growth stage detection. The
system’s prowess is exemplified with Butterhead lettuce, where the
model’s insights guide nutrient dosing in an aeroponic tower.
Integrating Arduino Uno for data acquisition and actuator control, with
Central Computing Unit (Orange Pi 5) for backend management and deep
learning application, the system’s architecture is robust and
comprehensive. An Android application complements the system, offering
real-time sensor data visualization, aeroponic tower initialization, and
plant location tracking. The system also provides location-based growth
recommendations, greenhouse-specific advice, and companion planting
suggestions. In a month, the system projected a yield of 40 plants
across 11 varieties. Drawing insights from various research papers, this
system epitomizes the fusion of technology and agriculture, offering a
promising solution for controlled environment agriculture and precision
farming. This research not only advances soil-less farming practices but
also addresses the increasing demand for organic food, presenting an
efficient solution for large-scale cultivation.